CN111325575A

CN111325575A - Question information recommendation method and device, computer equipment and storage medium

Info

Publication number: CN111325575A
Application number: CN202010093072.0A
Authority: CN
Inventors: 曾梦蕾; 陈冕
Original assignee: Beijing Missfresh Ecommerce Co Ltd
Current assignee: Beijing Missfresh Ecommerce Co Ltd
Priority date: 2020-02-14
Filing date: 2020-02-14
Publication date: 2020-06-23

Abstract

The embodiment of the application discloses a problem information recommendation method and device, computer equipment and a storage medium, and belongs to the technical field of internet. The method comprises the following steps: when the currently displayed target interface meets the evaluation collection condition, acquiring a historical order of a user identifier, wherein the historical order comprises a plurality of objects; selecting target evaluation objects with the quantity not more than a first preset quantity from historical orders; and acquiring target problem information associated with the target evaluation object, and displaying the target problem information, wherein the target problem information is used for a user to evaluate the target evaluation object. By means of the selection mode, the problem information recommended to the user can be effectively reduced, the time consumed by the user in evaluation based on the problem information is shortened, the evaluation willingness of the user is improved, the number of the collected evaluation information is increased, and the accuracy and the objectivity of the evaluation information are improved.

Description

Question information recommendation method and device, computer equipment and storage medium

Technical Field

The embodiment of the application relates to the technical field of internet, in particular to a problem information recommendation method and device, computer equipment and a storage medium.

Background

With the rapid development of the internet technology, a shopping mode based on the internet is more and more popular in daily life of people, and in order to optimize an online shopping process, improve the shopping experience of users, attract more users, and obtain the evaluation information of the users on completed orders.

In the related art, each time a user completes one order, an evaluation interface of the order can be displayed, and since the order includes a plurality of items of information such as purchased commodities, distributors, and packagers, the user can evaluate each item of information such as the commodities, the distributors, and the packagers in the evaluation interface.

In the evaluation process, the user needs to evaluate multiple items of information of the order, the time consumption is too long, and the evaluation intention of the user is low, so that the quantity of the obtained evaluation information is small, and the accuracy is low.

Disclosure of Invention

The embodiment of the application provides a problem information recommendation method and device, a computer device and a storage medium, which can effectively reduce problem information recommended to a user through screening and improve evaluation willingness of the user. The technical scheme is as follows:

in one aspect, a method for recommending question information is provided, and the method includes:

when a currently displayed target interface meets evaluation collection conditions, acquiring a historical order of a user identifier, wherein the historical order comprises a plurality of objects;

selecting target evaluation objects with the quantity not more than a first preset quantity from the historical orders;

and acquiring target problem information associated with the target evaluation object, and displaying the target problem information, wherein the target problem information is used for a user to evaluate the target evaluation object.

Optionally, each order comprises objects belonging to a plurality of dimensions; selecting target evaluation objects with the quantity not more than a first preset quantity from the historical orders, wherein the target evaluation objects comprise:

selecting dimensions not exceeding a second preset number from multiple dimensions of the historical order as target dimensions, wherein the second preset number is not more than the first preset number;

and selecting objects which belong to the target dimension and do not exceed the first preset number from the plurality of objects of the historical order as the target evaluation objects.

Optionally, the selecting, from the multiple dimensions of the historical order, a dimension not exceeding a second preset number as a target dimension includes:

acquiring a first preset selection probability of each dimension in the plurality of dimensions;

and selecting dimensions not exceeding the second preset number from the multiple dimensions as the target dimensions according to the first preset selection probability of each dimension.

acquiring a first evaluation rate difference value of each dimension in the plurality of dimensions, wherein the first evaluation rate difference value is used for representing the difference between an evaluation rate target value and an evaluation rate actual value of the corresponding dimension;

according to the sequence of the first evaluation difference values from large to small, selecting dimensions which are not more than the second preset number from the multiple dimensions as the target dimensions, or determining the dimensions which are larger than the first evaluation difference value and not more than the second preset number from the multiple dimensions as the target dimensions.

Optionally, the selecting, from the historical orders, target evaluation objects not exceeding a first preset number includes:

acquiring a second preset selection probability of each object in the historical order;

and selecting objects not exceeding the first preset number from the plurality of objects as the target evaluation objects according to the second preset selection probability of each object.

Optionally, the acquiring target problem information associated with the target evaluation object includes:

acquiring a plurality of question information associated with the target evaluation object;

acquiring a second evaluation rate difference value of each question information in the plurality of question information, wherein the second evaluation rate difference value is used for representing the difference between an evaluation rate target value and an evaluation rate actual value of the corresponding question information;

and selecting at least one piece of problem information from the plurality of pieces of problem information as the target problem information according to the sequence of the second evaluation rate difference from large to small, or determining at least one piece of problem information of the plurality of pieces of problem information, wherein the second evaluation rate difference is larger than a second preset threshold value, as the target problem information.

Optionally, the displaying the target issue information includes:

popping up a window on the target interface, and displaying the target question information in the window; or,

displaying a target link in the target interface, and displaying the target problem information when the trigger operation of the target link is detected; or,

and displaying the graphic code in the target interface, and displaying the target problem information when the identification operation of the graphic code is detected.

Optionally, after the displaying the target issue information, the method further includes:

and acquiring evaluation information input aiming at the target problem information.

Optionally, after obtaining the evaluation information input for the target issue information, the method further includes:

and acquiring the evaluation score of the target evaluation object according to the evaluation information, wherein the evaluation score is used for representing the evaluation level of the target evaluation object.

Optionally, the obtaining, according to the evaluation information, an evaluation score of the target evaluation object includes:

and performing score conversion processing on the evaluation information to obtain the evaluation score of the target evaluation object.

inquiring a preset database according to the evaluation information to obtain an evaluation score corresponding to the evaluation information, wherein the preset database comprises corresponding relations between various evaluation information and the evaluation score;

and determining the evaluation score of the target evaluation object according to the evaluation score corresponding to the evaluation information.

Optionally, the target evaluation object is an article, and the method further includes:

acquiring the exposure rate of the article according to the evaluation score of the article;

and displaying the article on an article recommendation interface according to the exposure rate.

Optionally, the target evaluation object is a service party, and the method further includes:

and according to the evaluation score of the server, carrying out order distribution on the server.

In another aspect, there is provided a question information recommending apparatus, the apparatus including:

the order acquisition module is used for acquiring a historical order of a user identifier when a currently displayed target interface meets evaluation collection conditions, wherein the historical order comprises a plurality of objects;

the selection module is used for selecting target evaluation objects with the number not more than a first preset number from the historical orders;

and the information display module is used for acquiring target problem information associated with the target evaluation object and displaying the target problem information, wherein the target problem information is used for a user to evaluate the target evaluation object.

Optionally, each order comprises objects belonging to a plurality of dimensions; the selecting module comprises:

the dimension selecting unit is used for selecting dimensions not exceeding a second preset number from multiple dimensions of the historical order as target dimensions, wherein the second preset number is not more than the first preset number;

and the first object selecting unit is used for selecting objects which belong to the target dimension and do not exceed the first preset number from a plurality of objects of the historical order as the target evaluation objects.

Optionally, the dimension selecting unit is further configured to obtain a first preset selecting probability of each of the plurality of dimensions;

the dimensionality selection unit is further configured to select, as the target dimensionality, a dimensionality not exceeding the second preset number from the multiple dimensionalities according to the first preset selection probability of each dimensionality.

Optionally, the dimension selecting unit is further configured to obtain a first evaluation rate difference value of each of the plurality of dimensions, where the first evaluation rate difference value is used to represent a difference between an evaluation rate target value and an evaluation rate actual value of a corresponding dimension;

the dimension selecting unit is further configured to select, as the target dimension, a dimension that is not greater than the second preset number from the multiple dimensions according to a descending order of the first evaluation value difference, or determine, as the target dimension, a dimension that is greater than a first preset threshold value and is not greater than the second preset number from the multiple dimensions.

Optionally, the selecting module includes:

the probability obtaining unit is used for obtaining a second preset selection probability of each object in the historical order;

and the second object selection unit is used for selecting objects which are not more than the first preset number from the plurality of objects as the target evaluation objects according to the second preset selection probability of each object.

Optionally, the information display module includes:

the problem acquisition unit is used for acquiring a plurality of problem information related to the target evaluation object;

a difference acquisition unit configured to acquire a second evaluation rate difference for each of the plurality of question information, the second evaluation rate difference being indicative of a difference between an evaluation rate target value and an evaluation rate actual value of the corresponding question information;

and the information selecting unit is used for selecting at least one piece of problem information from the plurality of pieces of problem information as the target problem information according to the sequence of the second evaluation rate difference from large to small, or determining at least one piece of problem information of the plurality of pieces of problem information, of which the second evaluation rate difference is larger than a second preset threshold value, as the target problem information.

Optionally, the information display module includes:

the window display unit is used for popping up a window on the target interface and displaying the target problem information in the window; or,

the link display unit is used for displaying a target link in the target interface and displaying the target problem information when the trigger operation of the target link is detected; or,

and the graphic code display unit is used for displaying the graphic code in the target interface and displaying the target problem information when the identification operation of the graphic code is detected.

Optionally, the apparatus further comprises:

and the evaluation acquisition module is used for acquiring evaluation information input aiming at the target problem information.

Optionally, the apparatus further comprises:

and the score acquisition module is used for acquiring the evaluation score of the target evaluation object according to the evaluation information, wherein the evaluation score is used for expressing the evaluation level of the target evaluation object.

Optionally, the score obtaining module includes:

and the score acquisition unit is used for performing score conversion processing on the evaluation information to obtain the evaluation score of the target evaluation object.

Optionally, the score obtaining module includes:

the query unit is used for querying a preset database according to the evaluation information to obtain an evaluation score corresponding to the evaluation information, and the preset database comprises corresponding relations between various evaluation information and the evaluation score;

and the score determining unit is used for determining the evaluation score of the target evaluation object according to the evaluation score corresponding to the evaluation information.

Optionally, the target evaluation object is an article, and the apparatus further includes:

the exposure rate acquisition module is used for acquiring the exposure rate of the article according to the evaluation score of the article;

and the article display module is used for displaying the article on an article recommendation interface according to the exposure rate.

Optionally, the target evaluation object is a service provider, and the apparatus further includes:

and the distribution module is used for carrying out order distribution on the service party according to the evaluation score of the service party.

In another aspect, a computer device is provided, which includes a processor and a memory, wherein at least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor to implement the question information recommending method according to the above aspect.

In another aspect, a computer-readable storage medium is provided, in which at least one program code is stored, the at least one program code being loaded and executed by a processor to implement the question information recommending method according to the above aspect.

The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:

according to the method, the device, the computer equipment and the storage medium provided by the embodiment of the application, when a currently displayed target interface meets evaluation collection conditions, historical orders of user identification are obtained, target evaluation objects with the number not more than a first preset number are selected from the historical orders, target problem information related to the target evaluation objects is obtained, the target problem information is displayed, evaluation information input aiming at the target problem information is obtained, and evaluation scores of the target evaluation objects are obtained according to the evaluation information. By means of selecting the target evaluation object, problem information recommended to the user can be effectively reduced, time consumed when the user evaluates based on the problem information is shortened, evaluation willingness of the user is improved, the number of collected evaluation information is increased, and accuracy and objectivity of the evaluation information are improved.

In addition, the method provided by the embodiment of the application can also dynamically adjust the mode of selecting the target evaluation object and the target problem information, can control the flow distribution of the problem information in the mode of selecting the target evaluation object and the target problem information according to the selection probability or the evaluation rate difference value, focuses on the object and the problem information which are more concerned by the online shopping platform, and has more pertinence.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a flowchart of a problem information recommendation method provided in an embodiment of the present application;

FIG. 2 is a flowchart of another problem information recommendation method provided by an embodiment of the present application;

FIG. 3 is a schematic diagram illustrating a display of a window provided by an embodiment of the present application;

FIG. 4 is a schematic illustration of a link display provided by an embodiment of the present application;

fig. 5 is a schematic display diagram of a two-dimensional code provided in an embodiment of the present application;

FIG. 6 is a schematic illustration of a display of an evaluation interface provided by an embodiment of the present application;

FIG. 7 is a schematic illustration of a display of another evaluation interface provided by an embodiment of the present application;

FIG. 8 is a flowchart of another problem information recommendation method provided by an embodiment of the present application;

FIG. 9 is a flowchart of another problem information recommendation method provided in an embodiment of the present application;

fig. 10 is a schematic structural diagram of an issue information recommendation device according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of another problem information recommendation device provided in an embodiment of the present application;

fig. 12 is a schematic structural diagram of a terminal according to an embodiment of the present application;

fig. 13 is a schematic structural diagram of a server according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application will be further described in detail with reference to the accompanying drawings.

With the development of internet technology, the way of online shopping based on the internet is more and more popular, and the online shopping platform can provide various articles or services for users, thereby bringing great convenience to the users. The shopping experience of the user also plays a promoting role in the improvement and development of the online shopping platform, so that the acquisition of the shopping experience of the user is very important. The shopping experience of the user can be obtained through the evaluation information of the user, and the evaluation information is obtained after the user evaluates the information items included in the completed order.

In the related art, each time a user completes an order, the online shopping platform pushes a plurality of items of information included in the order, such as commodities, distributors, packagers and the like, to the user, and the user needs to evaluate each item of information one by one and submit the evaluation information to the online shopping platform. The evaluation method consumes too long time, can eliminate the interest of the user, improves the time cost of the user, reduces the evaluation intention of the user, and further leads the user to directly choose to ignore the evaluation process. In this case, most users willing to evaluate are users who have extreme experience on the online shopping platform, and the evaluation information submitted by the users is too comprehensive and subjective, and lacks objectivity. For the online shopping platform, the collected evaluation information is small in quantity and low in accuracy, and cannot provide enough basis for optimizing and perfecting the online shopping platform.

Therefore, the embodiment of the application provides a question information recommendation method, which is implemented by obtaining a history order of a user identifier when a currently displayed target interface meets an evaluation collection condition, selecting a part of objects from a plurality of objects included in the history order as target evaluation objects, obtaining a part of question information from question information associated with the target evaluation objects as target question information, and displaying the target question information for a user to evaluate the target evaluation objects. By means of selecting the target evaluation object, the problem information recommended to the user can be effectively reduced, the time consumed by the user in evaluation based on the problem information is shortened, the evaluation willingness of the user is improved, the collected evaluation information can be increased, and the accuracy of the evaluation information is improved.

The method can be applied to computer equipment, the computer equipment comprises a terminal and also comprises a server, the terminal can be a mobile phone, a computer, a tablet computer and the like, and the server can be a server, a server cluster consisting of a plurality of servers or a cloud computing service center.

When the computer equipment comprises a terminal, the terminal acquires a historical order of a user identifier when a currently displayed target interface meets an evaluation collection condition, selects a target evaluation object from the historical order, acquires target problem information associated with the target object, and displays the target problem information for a user to evaluate.

When the computer equipment comprises a terminal and a server, the terminal sends an information acquisition request to the server when a currently displayed target interface meets evaluation collection conditions, the information acquisition request carries a user identifier, the server acquires a historical order of the user identifier according to the received information acquisition request, selects a target evaluation object from the historical order, acquires target problem information associated with the target object, sends the target problem information to the terminal, and displays the target problem information by the terminal for the user to evaluate.

The problem information recommendation method provided by the embodiment of the application can be applied to a scene of recommending problem information to a user or collecting evaluation information of the user. By adopting the problem information recommendation method provided by the embodiment of the application, the computer equipment selects the object included in the historical order of the user identification and the problem information related to the object, reduces the problem information recommended to the user, improves the evaluation willingness of the user, and the user corresponding to the subsequent user identification can evaluate based on the checked problem information.

Fig. 1 is a flowchart of a problem information recommendation method provided in an embodiment of the present application, applied to a computer device, and referring to fig. 1, the embodiment includes:

in step 101, when the currently displayed target interface meets the evaluation collection condition, the computer device obtains a history order of the user identifier, wherein the history order comprises a plurality of objects.

In step 102, the computer device selects target evaluation objects not exceeding a first preset number from the historical orders.

In step 103, the computer device obtains target question information associated with the target evaluation object and displays the target question information, wherein the target question information is used for a user to evaluate the target evaluation object.

According to the method provided by the embodiment of the application, when the currently displayed target interface meets the evaluation collection condition, the historical order of the user identification is obtained, the historical order comprises a plurality of objects, target evaluation objects with the number not more than a first preset number are selected from the historical order, target problem information related to the target evaluation objects is obtained, and the target problem information is displayed for the user to evaluate the target evaluation objects. By selecting the objects included in the historical orders identified by the user and the problem information associated with the objects, the problem information recommended to the user can be reduced, the evaluation willingness of the user is improved, the quantity of the evaluation information which can be obtained is increased, and the accuracy and the objectivity of the evaluation information are improved.

In one possible implementation, each order includes objects belonging to multiple dimensions; selecting target evaluation objects with the quantity not more than a first preset quantity from historical orders, wherein the target evaluation objects comprise:

selecting objects which belong to target dimensions and do not exceed a first preset number from a plurality of objects of the historical order as target evaluation objects.

In another possible implementation manner, selecting dimensions not exceeding a second preset number from a plurality of dimensions of the historical order as target dimensions includes:

acquiring a first preset selection probability of each dimension in a plurality of dimensions;

and selecting dimensions not exceeding a second preset number from the multiple dimensions as target dimensions according to the first preset selection probability of each dimension.

acquiring a first evaluation rate difference value of each dimension in a plurality of dimensions, wherein the first evaluation rate difference value is used for representing the difference between an evaluation rate target value and an evaluation rate actual value of the corresponding dimension;

according to the sequence of the first evaluation difference values from large to small, selecting dimensions which are not more than a second preset number from the multiple dimensions as target dimensions, or determining the dimensions which are larger than a first preset threshold value and are not more than the second preset number from the multiple dimensions as the target dimensions.

In another possible implementation manner, selecting, from the historical orders, target evaluation objects not exceeding a first preset number includes:

and selecting objects not exceeding the first preset number from the plurality of objects as target evaluation objects according to the second preset selection probability of each object.

In another possible implementation manner, the obtaining target problem information associated with the target evaluation object includes:

acquiring a plurality of question information associated with a target evaluation object;

and selecting at least one piece of problem information from the plurality of problem information as target problem information according to the sequence of the second evaluation rate difference values from large to small, or determining at least one piece of problem information of which the second evaluation rate difference value is larger than a second preset threshold value from the plurality of pieces of problem information as the target problem information.

In another possible implementation, displaying the target issue information includes:

popping up a window on a target interface, and displaying target problem information in the window; or,

displaying a target link in a target interface, and displaying target problem information when the trigger operation of the target link is detected; or,

and displaying the graphic code in the target interface, and displaying target problem information when the identification operation of the graphic code is detected.

In another possible implementation manner, after the target issue information is displayed, the method further includes:

evaluation information input for the target problem information is acquired.

In another possible implementation manner, after obtaining the evaluation information input for the target issue information, the method further includes:

and acquiring the evaluation score of the target evaluation object according to the evaluation information, wherein the evaluation score is used for indicating the evaluation level of the target evaluation object.

In another possible implementation manner, obtaining the evaluation score of the target evaluation object according to the evaluation information includes:

In another possible implementation manner, the target evaluation object is an article, and the method further includes:

acquiring the exposure rate of the article according to the evaluation value of the article;

In another possible implementation manner, the target evaluation object is a server, and the method further includes:

Fig. 2 is a flowchart of a problem information recommendation method provided in an embodiment of the present application, and is applied to a computer device, where the computer device may include a terminal or a server, which is not limited in this embodiment of the present application.

Referring to fig. 2, the embodiment includes:

201. and when the currently displayed target interface meets the evaluation collection condition, the computer equipment acquires the historical order of the user identification.

In order to avoid interference brought to the user by frequently recommending the problem information to the user, the computer device may preset evaluation collection conditions, and recommend the problem information to the user only when the current condition meets the evaluation collection conditions. The evaluation collection condition can be set according to time, can also be set according to the access path of the user on the online shopping platform, and can also be determined according to the currently displayed interface.

For example, the evaluation collection condition is determined according to the currently displayed interface, and the evaluation collection condition is determined to be met by the target interface when the currently displayed target interface is any interface in the plurality of interfaces.

The user identifier is an identifier used for determining a unique user, and may be an account number for logging in the online shopping platform for the user, an identity card number of the user, a mobile phone number of the user, or the like. The embodiment of the application does not limit the specific form of the user identifier.

The number of users facing the same online shopping platform is huge, different users have different orders, and purchased goods or services are different. Therefore, in the embodiment of the present application, one of the user identifiers is taken as an example, and a process of recommending question information to the user corresponding to the user identifier is described.

In order to be able to improve the accuracy of the obtained evaluation information, the computer device should recommend matching problem information to the user, the matching problem information being associated with the goods and services actually purchased by the user. If the recommended problem information does not match, the reliability and accuracy of the evaluation information of the user are low. Therefore, the computer device can obtain the historical order of the user identification, and recommend the problem information to the user corresponding to the user identification according to the historical order.

In the embodiment of the application, the evaluation collection condition is set according to the interface, so that the computer device firstly judges whether the currently displayed target interface meets the evaluation collection condition, and when the currently displayed target interface meets the evaluation collection condition, the computer device obtains the historical order of the user identifier.

Where a historical order identifies a completed order for a user, the historical order includes a plurality of objects, such as items purchased, packagers and dispatchers of the order, and the like. In addition, the historical orders may also include order detail information, which may include the order amount, the time of generation of the order, the name, quantity, and price of the purchased item, and the like. Moreover, since the historical orders of different user identifiers are different, and the historical orders completed by the same user identifier at different times are also different, each historical order has an order identifier, and the order identifier is an identifier capable of determining a unique order, for example, the order identifier may be an order number generated in a time sequence.

In a possible implementation manner, a plurality of interfaces meeting the evaluation collection condition are preset, and when the target interface currently displayed by the computer is any one of the interfaces, it can be indicated that the target interface currently displayed meets the evaluation collection condition, and at this time, the computer device obtains the historical order of the user identifier.

In one possible implementation, the user identifier may have a plurality of historical orders for the same user identifier, and in order to avoid recommending repeated problem information to a user corresponding to the user identifier, the computer device may select an unevaluated historical order from the plurality of historical orders.

In another possible implementation manner, in order to improve the accuracy of the evaluation information, the computer device may further select, from the plurality of historical orders identified by the user, a historical order with a shorter time interval with the current time, such as a historical order with a shortest time interval with the current time, or a historical order randomly selected from the plurality of historical orders within a preset time period before the current time.

For example, when the currently displayed target interface meets the evaluation collection condition, the last historical order identified by the user is obtained.

202. And selecting target evaluation objects with the quantity not more than a first preset quantity from the historical orders by the computer equipment.

Since the history order includes a plurality of objects, if each object is evaluated, it takes too long time, which impairs the user's patience, and thus the evaluation will be reduced, and the evaluation process may be selected to be directly ignored. Therefore, in the embodiment of the present application, it is not necessary to evaluate all objects, and only a part of objects may be selected from a plurality of objects included in the history order.

After the historical orders of the user identification are obtained, the computer equipment selects objects with the quantity not more than a first preset quantity from the historical orders as target evaluation objects. The first preset number is smaller than the total number of all objects included in the historical order.

The process of selecting the target evaluation object from the historical order by the computer equipment at least comprises the following conditions:

(1) selecting dimensionality, and then selecting a target evaluation object according to the dimensionality:

in one possible implementation, each order includes objects belonging to a plurality of dimensions, and the process of selecting, by the computer device, a target evaluation object not exceeding a first preset number from the historical orders includes: the computer equipment selects dimensions not exceeding a second preset number from a plurality of dimensions of the historical order as target dimensions, and selects objects belonging to the target dimensions and not exceeding the first preset number from a plurality of objects included in the historical order as target evaluation objects. Wherein the second preset number is not greater than the first preset number.

The information of the historical order in different dimensions is used for describing the historical order from different aspects, and the dimension of the historical order may be at least one of a commodity dimension, a distributor dimension, a packager dimension and a customer service staff dimension, and may also include other dimensions, which is not specifically limited in this embodiment of the present application.

It should be noted that the number of the target dimensions may be one or more, and if the number of the target dimensions is one, the first preset number of target evaluation objects selected from the historical orders by the computer device all belong to the dimension; if the number of the target dimensions is multiple, the computer device selects multiple target evaluation objects from the historical orders and respectively belongs to any one of the multiple dimensions.

The method for selecting the target dimension from the multiple dimensions of the historical order can be a random selection method, an equal proportion selection method, a selection method according to a preset probability, or other selection methods.

(1-1) selecting a target dimension according to the selection probability:

in this case, the computer device may set a selection probability for each of the plurality of dimensions in advance, and may subsequently select a target dimension according to the preset selection probability. In the embodiment of the present application, the preset selection probability for each dimension is a first preset selection probability, and a process of selecting a dimension not exceeding a second preset number as a target dimension from a plurality of dimensions of a historical order includes: the computer equipment obtains a first preset selection probability of each dimension in the multiple dimensions, and selects dimensions not more than a second preset number from the multiple dimensions as target dimensions according to the first preset selection probability of each dimension in the multiple dimensions.

For example, the historical order includes dimension 1, dimension 2, and dimension 3, and the first preset selection probabilities of dimension 1, dimension 2, and dimension 3 are 20%, 30%, and 50%, respectively, when the target dimension is selected according to the first preset selection probability, dimension 1 is selected as the target dimension with a probability of 20%, dimension 2 is selected as the target dimension with a probability of 30%, and dimension 3 is selected as the target dimension with a probability of 50%.

Different selection probabilities can be set for different dimensions, different target dimensions can be selected at each time according to the mode of selecting the target dimensions according to the selection probabilities, and dynamic adjustment of the target dimensions is achieved. And the selection probability is set for the dimensionality, so that the flow distribution of the problem information can be controlled finally, the emphasis is focused on the object and the problem information concerned by the online shopping platform, and the pertinence of the target problem information is enhanced.

(1-2) selecting a target dimension according to the evaluation rate difference:

in another possible implementation manner, each of the multiple dimensions has an evaluation rate target value, the evaluation rate target value may be a preset value or a randomly assigned value, and the evaluation rate target values of different dimensions may be different values, may also be partially identical values, or may also be completely identical values.

Selecting dimensions not exceeding a second preset number from multiple dimensions of the historical order as target dimensions, wherein the process comprises the following steps: the computer equipment obtains a first evaluation rate difference value of each dimension of multiple dimensions of the historical order, and selects a target dimension from the multiple dimensions according to the first evaluation rate difference value.

Wherein the process of the computer device obtaining the first evaluation rate difference value of each of the plurality of dimensions comprises: and acquiring an evaluation rate target value and an evaluation rate actual value of each dimension, and acquiring a first evaluation rate difference value of each dimension according to the evaluation rate target value and the evaluation rate actual value of each dimension. The evaluation rate target value of the dimension is used for representing a value which is expected to be reached by the evaluation rate of the corresponding dimension, the evaluation rate actual value of the dimension is used for representing a current value of the evaluation rate of the corresponding dimension, and the first evaluation rate difference value is used for representing the difference between the evaluation rate target value and the evaluation rate actual value of the corresponding dimension.

In one possible implementation, the selecting a target dimension from the plurality of dimensions according to the first evaluation rate difference may include: and sorting the plurality of dimensions according to the sequence of the first evaluation difference value from large to small, and selecting the dimensions not more than a second preset number from the plurality of dimensions according to the sorting as target dimensions.

For example, the second preset number is 3, and the computer device selects the first 3 dimensions from the plurality of dimensions as the target dimensions in the descending order.

In another possible implementation manner, the selecting a target dimension from the multiple dimensions according to the first evaluation rate difference may include: the computer equipment obtains a preset first preset threshold value, and determines the dimensionalities of which the first evaluation difference values are larger than the first preset threshold value and the number is not more than a second preset number as target dimensionalities.

For example, the second preset number is 3, the first preset threshold is 50, the computer device selects 5 dimensions of which the first evaluation rate difference is greater than 50 from the multiple dimensions, and randomly selects 2 dimensions from the 5 dimensions to determine the dimension as the target dimension.

(2) Selecting a target evaluation object directly from a plurality of objects:

in this case, the computer device may set a selection probability for a plurality of objects of the historical order in advance, and may subsequently select the target evaluation object from the plurality of objects according to the preset selection probability. In the embodiment of the present application, the preset selection probability for each object is a second preset selection probability, and a process of selecting target evaluation objects not exceeding a first preset number from a history order includes: and the computer equipment acquires the second preset selection probability of each object in the plurality of objects included in the historical order, and selects objects not exceeding the first preset number from the plurality of objects as target evaluation objects according to the second preset selection probability of each object.

Regarding the process of selecting the target evaluation object from the plurality of objects according to the second preset selection probability of each object, in one possible implementation manner, the plurality of objects are sorted in the order of the second preset selection probability from large to small, and the objects not exceeding the first preset number are selected from the plurality of objects according to the sorting as the target evaluation objects.

For example, the first preset number is 3, the historical order includes 5 objects, the second preset selection probabilities of the 5 objects are 10%, 15%, 20%, 25% and 30%, respectively, and after the 5 objects are arranged in descending order, 3 objects with the second preset selection probabilities of 30%, 25% and 20% are selected as the target evaluation objects.

203. And the computer equipment acquires target problem information associated with the target evaluation object.

To obtain rating information for a target rating object, a computer device may obtain rating information that a user may input for question information associated with the target rating object. And the target problem information is used for the user to evaluate the corresponding target evaluation object. Therefore, after acquiring the target evaluation object, the computer device acquires target problem information associated with the target evaluation object.

Based on the above step 202, the computer device obtains at least one target evaluation object, and for each target evaluation object, the target evaluation object may include a plurality of associated question information. The process of acquiring the target question information associated with the target evaluation object is essentially a process of selecting a part of question information from a plurality of question information associated with the target evaluation object as the target question information. In this step, the computer device will describe a process of acquiring target problem information associated with any target evaluation object, taking the target evaluation object as an example.

In one possible implementation manner, the question information has an evaluation rate target value, which may be a preset value or a randomly assigned value, and the evaluation rate difference values of different target evaluation objects may be different values from each other, may be partially identical values, or may be completely identical values.

The process of obtaining target problem information associated with the target evaluation object includes: the computer equipment acquires a plurality of question information related to the target evaluation object, acquires a second evaluation rate difference value of each question information in the plurality of question information, and selects target question information from the plurality of question information according to the second evaluation rate difference value.

The process of the computer device obtaining the second evaluation rate difference value of each question information in the plurality of question information comprises the following steps: and acquiring a second evaluation rate difference value of each question message according to the evaluation rate target value and the evaluation rate actual value of each question message. The evaluation rate target value of the question information is used for representing a numerical value which is expected to be reached by the evaluation rate of the corresponding question information, the evaluation rate actual value of the question information is used for representing a current numerical value of the evaluation rate of the corresponding question information, and the second evaluation rate difference value is used for representing a difference between the evaluation rate target value and the evaluation rate actual value of the corresponding question information.

In a possible implementation manner, the selecting the target question information from the plurality of question information according to the second evaluation rate difference may include: and sorting the plurality of problem information in the order of the second evaluation value difference from large to small, and selecting at least one problem information from the plurality of problem information as target problem information according to the sorting.

For example, the computer device selects the first two question information as the target question information from the plurality of question information in descending order.

In another possible implementation manner, the selecting the target question information from the plurality of question information according to the second evaluation rate difference may include: the computer equipment acquires a preset second preset threshold value, and determines at least one problem information of the plurality of problem information, of which the second evaluation rate difference is larger than the second preset threshold value, as the target problem information.

For example, the second preset threshold is 50, and the computer device selects 2 pieces of question information having a second evaluation rate difference larger than 50 from the plurality of pieces of question information to determine as the target question information.

When the number of target evaluation objects is plural, plural pieces of target problem information can be acquired for the plural target evaluation objects. If the number of the target evaluation objects is 1, the computer device may acquire one or more target problem information associated with the target evaluation object for the target evaluation object. Thus, the computer device may obtain at least one target issue information.

204. The computer device displays the target issue information.

After the at least one target question information is acquired, the computer device may display the at least one target question information, so that a user corresponding to the user identifier evaluates the target evaluation object based on the at least one target question information.

In one possible implementation, the computer device displays the acquired at least one target issue information, including at least one of:

(1) referring to fig. 3, the computer device pops up a window on the currently displayed target interface, and displays the acquired at least one target question information in the window.

In one possible implementation, the computer device may also pop up a dialog box in the target interface in which the at least one target issue information is displayed.

(2) And the computer equipment displays a target link in the currently displayed target interface, wherein the target link is a link corresponding to the target problem information and is used for triggering the display of the target problem information. And when the computer equipment detects the trigger operation of the target link, displaying the acquired at least one piece of target problem information.

For example, referring to fig. 4, the computer device displays a link a in the target interface, and when a click operation on the link a is detected, jumps to an evaluation interface from the target interface, where the evaluation interface includes the acquired target question information.

(3) And the computer equipment displays the graphic code in the currently displayed target interface, wherein the graphic code is the graphic code corresponding to the target problem information and is used for triggering and displaying the target problem information. And when the computer equipment detects the identification operation of the graphic code, displaying the acquired at least one target problem information.

The graphic code can be a one-dimensional bar code, a two-dimensional code or other graphic codes used for determining and displaying the target problem information. And the graphic code is used for determining a unique display interface, and the display interface is an interface for displaying the target problem information.

For example, referring to fig. 5, the computer device displays a two-dimensional code in a target interface, and when a long-press operation on the two-dimensional code is detected, switches from the target interface to an evaluation interface, where the evaluation interface includes the acquired target problem information.

205. The computer device acquires evaluation information input for the target problem information.

The user can view at least one target question message displayed by the computer device, and can also evaluate based on the at least one target question message, and the computer device can acquire evaluation information input by the user aiming at the at least one target question message.

In one possible implementation, the at least one target issue information is in a text input form, each target issue information has a corresponding input field, and the computer device may acquire information input by the user in the input field as evaluation information input for the target issue information.

For example, referring to fig. 6, the evaluation interface includes target question information 1 and target question information 2, and an input field a corresponding to the target question information 1 and an input field B corresponding to the target question information 2, and the evaluation interface also includes a submit option. When a click operation on the submit option is detected, the computer apparatus acquires information C input in the input field a as evaluation information input for the target issue information 1, and acquires information D input in the input field B as evaluation information input for the target issue information 2.

In another possible implementation manner, the at least one target question information is in a selection form, each target question information further has a plurality of option information, and the computer device may acquire the option information selected by the user from the plurality of option information, and use the option information as the evaluation information input for the target question information.

For example, referring to fig. 7, the evaluation interface includes target question information 1 and 4 corresponding option information, target question information 2 and 3 corresponding option information, and also includes a submit option. When the click operation on the submission option is detected, the computer device acquires option information a1 in a selected state from among 4 pieces of option information corresponding to target question information 1, determines the option information a1 as evaluation information input for the target question information 1, further acquires option information C2 in a selected state from among 3 pieces of option information corresponding to target question information 2, and determines the option information C2 as evaluation information input for the target question information 2.

206. And the computer equipment acquires the evaluation score of the target evaluation object according to the evaluation information.

After the evaluation information aiming at the target problem information is acquired, in order to more intuitively know the evaluation level of the target evaluation object associated with the target problem information by the user, the computer equipment acquires the evaluation score of the target evaluation object according to the acquired evaluation information, wherein the evaluation score is used for representing the evaluation level of the target evaluation object by the user.

In one possible implementation manner, the process of obtaining the evaluation score of the target evaluation object according to the evaluation information includes: the computer equipment performs score conversion processing on the acquired evaluation information to obtain a score corresponding to the evaluation information, and the evaluation information is input aiming at the problem information related to the target evaluation object, so that the evaluation score of the target evaluation object can be acquired according to the acquired score.

In another possible implementation manner, the computer device stores a preset database in advance, and the preset database includes a plurality of corresponding relations between the evaluation information and the evaluation scores. The process of obtaining the evaluation score of the target evaluation object according to the evaluation information includes: and the computer equipment queries the preset database according to the evaluation information to obtain an evaluation score corresponding to the evaluation information, and determines the evaluation score of the target evaluation object according to the evaluation score corresponding to the evaluation information.

Because different target problem information may have different forms, evaluation information input for different forms of target problem information is also different, and the process of performing score conversion processing on the evaluation information is also different. The form of the target problem information may be a text input form, a selection form or other forms, and the form of the target problem information is not specifically limited in the embodiments of the present application.

According to different forms of the target problem information, the process of performing score conversion processing on the evaluation information at least comprises the following conditions:

(1) the target question information is in the form of text input:

in this case, the process of the computer device performing score conversion processing on the evaluation information includes: the computer equipment extracts the characteristics of the obtained evaluation information to obtain sentence characteristics, the sentence characteristics are used for representing the content of the evaluation information, and the score corresponding to the sentence characteristics is determined as an evaluation score.

The sentence characteristics may include semantic characteristics and structural characteristics of the evaluation information, the semantic characteristics are used for indicating the meaning of the evaluation information, and the structural characteristics are used for indicating the sentence structure adopted by the evaluation information.

When the sentence characteristics are extracted, the computer device may perform word segmentation on the evaluation information to obtain at least one word group included in the evaluation information, and convert the at least one word group into at least one corresponding word vector, where the at least one word vector may be used as the sentence characteristics of the evaluation information. When the evaluation information is segmented, a conditional random field algorithm or other segmentation algorithms can be adopted, and for each phrase, a preset conversion algorithm can be adopted to convert the phrase into a word vector, so as to obtain the word vector corresponding to the phrase. The predetermined translation algorithm may be a word2vec algorithm or other algorithm.

In the embodiment of the application, the sentence characteristics obtained by extracting the characteristics of the evaluation information comprise evaluation semantic characteristics and structural characteristics, and the computer equipment can determine the corresponding evaluation score according to the sentence characteristics.

In one possible implementation manner, the computer device stores a preset database in advance, wherein the preset database comprises corresponding relations between various evaluation semantic features and evaluation scores. After obtaining the evaluation semantic features, the computer equipment queries a preset database according to the evaluation semantic features and determines evaluation scores corresponding to the evaluation semantic features. Then, the computer device determines whether to determine the evaluation score as the evaluation score corresponding to the evaluation information according to the structural feature in the sentence feature.

For example, if the target question information is "the taste of strawberry is good? "the evaluation information input for the target problem information is" bad taste ", and the preset database at least includes the corresponding relation: and if the score is 'good- + 5', 'general-0' and 'not good-' 5 ', the score corresponding to the keyword' not good '-5' can be determined as the evaluation score corresponding to the evaluation information by extracting the characteristics and inquiring a preset database.

(2) The target question information is in the form of a selection:

in this case, the target question information also has a plurality of option information, and the computer device may set in advance a score value corresponding to each option information. The process of performing score conversion processing on the evaluation information comprises the following steps: and the computer equipment acquires the option information in the selected state in the target problem information and determines the score corresponding to the option information as the evaluation score.

In one possible implementation manner, the computer device stores a preset database in advance, and the preset database includes a corresponding relationship between the option information and the evaluation score. After the computer equipment acquires the option information in the selected state, a preset database is inquired according to the option information, the evaluation score corresponding to the option information is determined, and the evaluation score is determined as the evaluation score corresponding to the evaluation information.

After determining the rating score corresponding to the rating information, the computer device may determine a rating score of the target rating object according to the rating score. If the target evaluation object only has one piece of target problem information and the computer equipment only acquires one piece of evaluation information input aiming at the target problem information, the evaluation score corresponding to the evaluation information is the evaluation score of the target evaluation object. However, in order to obtain a more accurate evaluation score of a target evaluation object, the computer device may obtain evaluation information input for a plurality of target question information associated with the target evaluation object, or obtain a plurality of evaluation information input for one target question information associated with the target evaluation object.

In a possible implementation manner, for the same target evaluation object, the computer device obtains multiple pieces of target problem information associated with the target evaluation object, obtains, according to evaluation information input for each piece of target problem information, an evaluation score corresponding to each piece of evaluation information, and obtains multiple evaluation scores, where the evaluation score of the target evaluation object is determined according to the multiple evaluation scores. The evaluation score of the target evaluation object may be obtained by summing the evaluation scores, may also be obtained by weighting and summing the evaluation scores, and may also be obtained by other ways, which is not specifically limited in this embodiment of the present application.

And under the condition of adopting a weighted summation mode, the computer equipment calculates the evaluation score of the target evaluation object according to the weight of each target question information and the corresponding evaluation score. The weight may be preset, may be determined by counting the attention degree of the user or the online shopping platform to the target question information, or may be determined in other manners.

In another possible implementation manner, users corresponding to multiple user identifiers may evaluate the same target evaluation object, and input evaluation information for target question information associated with the target evaluation object, at this time, the computer device obtains an evaluation score corresponding to each evaluation information to obtain multiple evaluation scores, and the evaluation score of the target evaluation object is determined according to the multiple evaluation scores. The evaluation score of the target evaluation object may be obtained by summing the plurality of evaluation scores, may also be obtained by weighting and summing the plurality of evaluation scores, and may also be obtained by other ways, which is not specifically limited in this embodiment of the present application.

In a weighted summation manner, the computer device may calculate the evaluation score of the target evaluation object according to the weight of each user identifier and the corresponding evaluation score. Alternatively, the computer device may also calculate the evaluation score of the target evaluation object in accordance with the weight of the evaluation time of each piece of evaluation information and the corresponding evaluation score.

In a possible implementation manner, the weight of the user identifier is determined according to the level of the user identifier, and the level of the user identifier may be determined according to the total purchase amount corresponding to the user identifier, the purchase amount corresponding to the user identifier, or the purchase frequency corresponding to the user identifier. The higher the total amount purchased, the amount purchased, or the frequency of purchases, the higher the rank of the user identification, and the lower the total amount purchased, the amount purchased, or the frequency of purchases, the lower the rank of the user identification. In addition, the weight of the user identifier may also be determined according to other manners, which is not specifically limited in this embodiment of the application.

In another possible implementation manner, considering that the evaluation of the user may change with the passage of time, that is, the evaluation information with the closer evaluation time can reflect the real will of the user, and the evaluation information with the farther evaluation time may not accurately express the current real will of the user, when the computer device acquires the evaluation information, the evaluation time of the evaluation information is also acquired, and the weight of the evaluation score is set according to the sequence of the evaluation time, where the weight is negatively related to the sequence of the evaluation time, that is, the earlier evaluation time, the lower the weight, and the later evaluation time, the higher the weight.

It should be noted that, in the embodiments of the present application, the evaluation score of the target evaluation object may be determined by considering any of the above factors alone, or may be determined by considering a plurality of the above factors together, or may be determined by randomly combining the above factors and considering the combined factors together.

After the computer device obtains the evaluation score of the target evaluation object, the evaluation score can be applied to an online shopping platform, for example, commodities are recommended according to the evaluation score of the target evaluation object, or order distribution is performed according to the evaluation score of the target evaluation object.

In a possible implementation manner, the target evaluation object is an article, and after the evaluation score of the target evaluation object is obtained, the computer device obtains the exposure rate of the article according to the evaluation score of the article, and displays the article on the article recommendation interface according to the exposure rate. The exposure rate is used for indicating the display condition of the article, the higher the exposure rate is, the more the number of times the article is displayed is, the longer the display time is, and the lower the exposure rate is, the less the number of times the article is displayed is, the shorter the display time is.

In another possible implementation manner, the target evaluation object is a server, and after obtaining the evaluation score of the target evaluation object, the computer device performs order allocation on the server according to the evaluation score of the server. Alternatively, the average reward for each order of the service party may also be determined based on the rating score of the service party. Or a reward and punishment mechanism can be formulated for the service side according to the evaluation score of the service side. For the historical order, the service party is a party providing services in the historical order, and may be a packager, a distributor, a customer service person, or the like.

For example, the service party is a distributor a, and after the computer device obtains the evaluation score of the distributor a, if the evaluation score of the distributor a is greater than a preset score, the computer device preferentially allocates the order with the higher distribution amount to the distributor a.

According to the method provided by the embodiment of the application, when a currently displayed target interface meets evaluation collection conditions, a historical order of a user identifier is obtained, target evaluation objects with the number not more than a first preset number are selected from the historical order, target problem information related to the target evaluation objects is obtained, the target problem information is displayed, evaluation information input aiming at the target problem information is obtained, and evaluation scores of the target evaluation objects are obtained according to the evaluation information. By means of selecting the target evaluation object, problem information recommended to the user can be effectively reduced, time consumed when the user evaluates based on the problem information is shortened, evaluation willingness of the user is improved, the number of collected evaluation information is increased, and accuracy and objectivity of the evaluation information are improved.

It should be noted that, in the method provided in the embodiment of the present application, although the number of question information recommended to each user is reduced, the question information may be split, and the evaluation will of the user is improved by reducing the number of question information, so that it is ensured that a sufficient number of and accurate evaluation information can be obtained for each object.

The method and the device for recommending the problem information can be applied to any scene of recommending the problem information or collecting the evaluation information. When the computer logs in based on the user identifier and enters a home page of the online shopping platform, target problem information determined according to a last historical order of the user identifier can be displayed based on the home page, and evaluation information input according to the target problem information and an evaluation score corresponding to the evaluation information are obtained. The target question information has a corresponding evaluation object, and the computer device superimposes the evaluation score on the current evaluation score of the evaluation object, so that the latest evaluation score of the evaluation object can be obtained.

If the evaluation object is an article, the computer device may determine the exposure rate of the article according to the evaluation score after the article is superimposed, and display the article in an article recommendation interface according to the exposure rate. If the evaluation object is a server, the computer device may determine an order allocation rule for the server according to the evaluation score superimposed by the server, and subsequently allocate an order for the server according to the order allocation rule.

Fig. 8 is a flowchart of a problem information recommendation method provided in an embodiment of the present application, where the problem information recommendation method is applied to a computer device, and referring to fig. 8, the method includes:

801. the computer device logs in the online shopping platform based on the user identification A and displays the home page of the online shopping platform.

Wherein, the home page of the online shopping platform meets the evaluation collection condition.

802. The computer device obtains the 3 dimensions (commodity dimension, packager dimension, and deliverer dimension) that an order includes on user identification a.

The commodity dimensions comprise an object 1-strawberry, an object 2-apple and an object 3-egg, the packager dimensions comprise an object 4-packager, and the distributor dimensions comprise an object 5-distributor. The preset selection probabilities of the 3 dimensions are 50%, 30% and 20%, respectively.

803. And the computer equipment determines the commodity dimension with the maximum preset selection probability as the target dimension.

The preset selection probabilities of 3 commodity dimensionalities are 40%, 30% and 30% respectively.

804. And the computer equipment determines the strawberry with the maximum preset selection probability as a target evaluation object.

Wherein, strawberry has 3 associated problem information, and problem information 1 is "how do the taste? "question information 2 is" how fresh? "how does question information 3 be" component? ". The evaluation rate target values of the 3 pieces of problem information are all 100, and the evaluation rate actual values are 19, 35 and 47, respectively.

805. The computer device calculates evaluation rate difference values of the 3 pieces of problem information, and determines the problem information 1 with the largest evaluation rate difference value as the target problem information.

The question information 1 has corresponding option information 1 "good", option information 2 "general", and option information 3 "bad".

806. The computer equipment displays an evaluation interface, and the interface comprises question information 1 and corresponding 3 option information.

807. When the selection operation of option information 1 is detected, the computer device determines option information 1 as the evaluation information input for the target question information, and superimposes the score "+ 1" corresponding to option information 1 on the current evaluation score 55 of the strawberry, resulting in the latest evaluation score 56 of the strawberry.

808. The computer device determines an exposure rate of the strawberries based on the rating score 56, and displays the strawberries in the goods recommendation interface according to the exposure rate.

Fig. 9 is a flowchart of a problem information recommendation method according to an embodiment of the present application, where the computer device includes a problem determination module and an information processing module.

The problem determination module is used for obtaining a previous historical order of the user identifier when logging in an online shopping platform based on the user identifier and entering a home page, selecting a target dimension from multiple dimensions of the historical order, selecting a target evaluation object from multiple objects of the target dimension, and selecting target problem information from problem information associated with the target evaluation object, so that the target problem information to be recommended to the user identifier is determined.

The information processing module is used for recommending the determined target problem information to a user corresponding to the user identification, acquiring evaluation information input by the user aiming at the displayed target problem information, analyzing and performing score conversion processing on the evaluation information, acquiring the evaluation score of the evaluation information, and superposing the evaluation scores of the target evaluation objects to obtain the total evaluation score of the target evaluation objects.

Fig. 10 is a schematic structural diagram of an issue information recommendation apparatus according to an embodiment of the present application, and as shown in fig. 10, the apparatus includes:

an order obtaining module 1001, configured to obtain a historical order of a user identifier when a currently displayed target interface meets an evaluation collection condition, where the historical order includes multiple objects;

a selecting module 1002, configured to select target evaluation objects of which the number is not more than a first preset number from a history order;

the information display module 1003 is configured to acquire target problem information associated with the target evaluation object and display the target problem information, where the target problem information is used for a user to evaluate the target evaluation object.

The problem information recommendation device provided by the embodiment of the application acquires a history order of a user identifier when a currently displayed target interface meets an evaluation collection condition, selects target evaluation objects with the number not more than a first preset number from the history order, acquires target problem information associated with the target evaluation objects, displays the target problem information, acquires evaluation information input aiming at the target problem information, and acquires an evaluation score of the target evaluation object according to the evaluation information. By means of the selection mode, the problem information recommended to the user can be effectively reduced, the time consumed by the user in evaluation based on the problem information is shortened, the evaluation willingness of the user is improved, the number of the collected evaluation information is increased, and the accuracy and the objectivity of the evaluation information are improved.

Alternatively, as shown in FIG. 11, each order includes objects belonging to multiple dimensions; the selecting module 1002 includes:

the dimension selecting unit 10021 is configured to select, as a target dimension, a dimension that does not exceed a second preset number from multiple dimensions of the historical order, where the second preset number is not greater than the first preset number;

the first object selecting unit 10022 is configured to select, from a plurality of objects in the historical order, an object that belongs to the target dimension and does not exceed a first preset number as a target evaluation object.

Optionally, as shown in fig. 11, the dimension selecting unit 10021 is further configured to obtain a first preset selection probability of each dimension of the multiple dimensions;

the dimension selecting unit 10021 is further configured to select, as a target dimension, a dimension that does not exceed a second preset number from the multiple dimensions according to the first preset selection probability of each dimension.

Optionally, as shown in fig. 11, the dimension selecting unit 10021 is further configured to obtain a first evaluation rate difference value of each of the multiple dimensions, where the first evaluation rate difference value is used to represent a difference between an evaluation rate target value and an evaluation rate actual value of the corresponding dimension;

the dimension selecting unit 10021 is further configured to select, as target dimensions, dimensions from the multiple dimensions that do not exceed a second preset number according to a descending order of the first evaluation value differences, or determine, as the target dimensions, dimensions from the multiple dimensions that the first evaluation value differences are greater than a first preset threshold and the number of the dimensions does not exceed the second preset number.

Optionally, as shown in fig. 11, the selecting module 1002 includes:

the probability obtaining unit 10023 is configured to obtain a second preset selection probability of each object in the historical order;

the second object selecting unit 10024 is configured to select, according to the second preset selection probability of each object, objects that do not exceed the first preset number from the multiple objects as target evaluation objects.

Optionally, as shown in fig. 11, the information display module 1003 includes:

a question acquisition unit 10031 configured to acquire a plurality of pieces of question information associated with the target evaluation object;

a difference obtaining unit 10032, configured to obtain a second evaluation rate difference value of each of the plurality of question information, where the second evaluation rate difference value is used to represent a difference between an evaluation rate target value and an evaluation rate actual value of the corresponding question information;

the information selecting unit 10033 is configured to select at least one piece of problem information from the plurality of pieces of problem information as the target problem information according to the sequence from large to small of the second evaluation rate difference, or determine at least one piece of problem information with a second evaluation rate difference larger than a second preset threshold value from the plurality of pieces of problem information as the target problem information.

Optionally, as shown in fig. 11, the information display module includes:

a window display unit 10034, configured to pop up a window on the target interface, and display the target question information in the window; or,

a link display unit 10035, configured to display a target link in the target interface, and when a trigger operation on the target link is detected, display target problem information; or,

the graphic code display unit 10036 is configured to display a graphic code in the target interface, and when an identification operation on the graphic code is detected, display target problem information.

Optionally, as shown in fig. 11, the apparatus further includes:

an evaluation acquisition module 1004 for acquiring evaluation information input for the target issue information.

Optionally, as shown in fig. 11, the apparatus further includes:

a score obtaining module 1005, configured to obtain, according to the evaluation information, an evaluation score of the target evaluation object, where the evaluation score is used to indicate a level of evaluation on the target evaluation object.

Optionally, as shown in fig. 11, the score obtaining module 1005 includes:

the score obtaining unit 10051 is configured to perform score conversion processing on the evaluation information to obtain an evaluation score of the target evaluation object.

Optionally, as shown in fig. 11, the score obtaining module 1005 includes:

the query unit 10052 is configured to query a preset database according to the evaluation information to obtain an evaluation score corresponding to the evaluation information, where the preset database includes a corresponding relationship between multiple kinds of evaluation information and the evaluation score;

the score determining unit 10053 is configured to determine an evaluation score of the target evaluation object according to the evaluation score corresponding to the evaluation information.

Alternatively, as shown in fig. 11, the target evaluation object is an article, and the apparatus further includes:

an exposure rate obtaining module 1006, configured to obtain an exposure rate of the article according to the evaluation score of the article;

an article display module 1007 is configured to display an article on the article recommendation interface according to the exposure rate.

Optionally, as shown in fig. 11, the target evaluation object is a service side, and the apparatus further includes:

and the distribution module 1008 is used for distributing the orders of the service parties according to the evaluation scores of the service parties.

Fig. 12 is a schematic structural diagram of a terminal according to an embodiment of the present application, which is capable of implementing operations executed by a computer device in the foregoing embodiments. The terminal 1200 may be a portable mobile terminal such as: the mobile terminal comprises a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, Moving Picture Experts compress standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts compress standard Audio Layer 4), a notebook computer, a desktop computer, a head-mounted device, a smart television, a smart sound box, a smart remote controller, a smart microphone, or any other smart terminal. Terminal 1200 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, and so forth.

In general, terminal 1200 includes: a processor 1201 and a memory 1202.

The processor 1201 may include one or more processing cores, such as a 4-core processor, an 8-core processor, or the like. Memory 1202 may include one or more computer-readable storage media, which may be non-transitory, for storing at least one instruction for processor 1201 to have for implementing the problem information recommendation methods provided by method embodiments herein.

In some embodiments, the terminal 1200 may further optionally include: a peripheral interface 1203 and at least one peripheral. The processor 1201, memory 1202, and peripheral interface 1203 may be connected by a bus or signal line. Various peripheral devices may be connected to peripheral interface 1203 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 1204, display 1205, and audio circuitry 1206.

The Radio Frequency circuit 1204 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuit 1204 communicates with a communication network and other communication devices by electromagnetic signals.

The display screen 1205 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. The display 1205 may be a touch display screen and may also be used to provide virtual buttons and/or a virtual keyboard.

Audio circuitry 1206 may include a microphone and a speaker. The microphone is used for collecting audio signals of a user and the environment, converting the audio signals into electric signals, and inputting the electric signals to the processor 1201 for processing or inputting the electric signals to the radio frequency circuit 1204 to achieve voice communication. For stereo capture or noise reduction purposes, multiple microphones may be provided at different locations of terminal 1200. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is used to convert the electrical signal from the processor 1201 or the radio frequency circuit 1204 into an audio signal.

Those skilled in the art will appreciate that the configuration shown in fig. 12 is not intended to be limiting of terminal 1200 and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.

Fig. 13 is a schematic structural diagram of a server 1300 according to an embodiment of the present application, where the server 1300 may generate a relatively large difference due to a difference in configuration or performance, and may include one or more processors (CPUs) 1301 and one or more memories 1302, where the memory 1302 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 1301 to implement the methods provided by the foregoing method embodiments. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input/output, and the server may also include other components for implementing the functions of the device, which are not described herein again.

The server 1300 may be used to perform the above-described problem information recommendation method.

The embodiment of the application also provides computer equipment, which comprises a processor and a memory, wherein at least one program code is stored in the memory, and the at least one program code is loaded by the processor and provided with the problem information recommendation method for realizing the embodiment.

The embodiment of the present application further provides a computer-readable storage medium, in which at least one program code is stored, and the at least one program code is loaded by a processor and has a problem information recommendation method for implementing the above embodiment.

The embodiment of the present application further provides a computer program, where at least one program code is stored in the computer program, and the at least one program code is loaded and executed by a processor, so as to implement the problem information recommendation method of the above embodiment.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only an alternative embodiment of the present application and should not be construed as limiting the present application, and any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for recommending question information, the method comprising:

2. The method of claim 1, wherein each order comprises objects belonging to a plurality of dimensions; selecting target evaluation objects with the quantity not more than a first preset quantity from the historical orders, wherein the target evaluation objects comprise:

3. The method of claim 2, wherein selecting as a target dimension from the plurality of dimensions of the historical order, a dimension not exceeding a second predetermined number comprises:

4. The method of claim 2, wherein selecting as a target dimension from the plurality of dimensions of the historical order, a dimension not exceeding a second predetermined number comprises:

5. The method according to claim 1, wherein said selecting no more than a first preset number of target evaluation objects from said historical orders comprises:

6. The method according to claim 1, wherein the obtaining target question information associated with the target evaluation object comprises:

7. The method of claim 1, wherein the displaying the target issue information comprises:

8. An issue information recommendation apparatus, characterized in that the apparatus comprises:

9. A computer device, characterized in that the computer device comprises a processor and a memory, wherein at least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor to implement the question information recommendation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having at least one program code stored therein, the at least one program code being loaded and executed by a processor to implement the question information recommendation method of any one of claims 1 to 7.