CN114861074A - User data analysis method and system - Google Patents
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Abstract
The invention relates to the technical field related to data analysis, and discloses a user data analysis method and a user data analysis system.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a user data analysis method and system.
Background
The analysis of the user data is a data analysis method which can quickly and accurately know the user portrait and the user group portrait, and the management of a manager on the user group can be facilitated through the analysis of the user data.
In the prior art, with the development of networking, the popularization of data content is very important in some industries, such as multimedia related industries, network sales related industries and the like, the expansion effect of a user group is determined by the data popularization effect, and most of the prior art adopts a similar content popularization mode to realize, so that the directionality and pertinence of the user group are lacked in actual use, more useless popularization is often existed, popularization resources are occupied, and the expected popularization effect cannot be achieved.
Disclosure of Invention
The present invention is directed to a method and system for analyzing user data, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a user data analysis system comprising:
the user data acquisition module is used for acquiring the open browsing behavior of an object user, screening the open browsing behavior according to a preset effective browsing judgment standard and acquiring a plurality of characteristic browsing behaviors, wherein the open browsing behavior is used for representing the browsing content of the object user and a browsing behavior record corresponding to the browsing content;
the category label generation module is used for acquiring a plurality of category labels of the characteristic browsing behaviors, counting the browsing frequency of the plurality of category labels according to a browsing behavior record, and acquiring the preference degree of the object user on the plurality of category labels, wherein the category labels are used for representing the content categories of the browsing content corresponding to the characteristic browsing behaviors;
the tag viscosity judging module is used for acquiring target browsing behaviors of the object user, acquiring a plurality of promotion tags of the target browsing behaviors, counting browsing frequency of the promotion tags, and acquiring the preference degree of the object user to the plurality of promotion tags, wherein the target browsing behaviors are used for representing the public content of an object to be promoted, and the promotion tags are used for representing the category of the public content;
and the user guidance generation module is used for screening the plurality of promotion labels and the category labels according to a preset promotion standard, acquiring the plurality of promotion labels and the category labels and generating a guidance promotion scheme, wherein the guidance promotion scheme is used for promoting the public contents corresponding to the promotion labels to the contents corresponding to the category labels and browsing users.
As a further scheme of the invention: the system further comprises an object user screening module, wherein the object user screening module specifically comprises:
the user acquisition unit is used for acquiring browsing users of the public content of the object to be promoted, and the browsing users are used for representing all users who browse the public content;
and the user screening unit is used for acquiring the browsing behavior record of the browsing user on the public content, judging and screening the content total ratio of the public content according to the browsing behavior record, and acquiring a plurality of object users, wherein the content total ratio is used for representing the browsing completion degree of the browsing user on a certain public content, and the object users are the browsing users with the content total ratio reaching a preset value.
As a further scheme of the invention: the category label generation module comprises:
the category acquisition unit is used for acquiring the category label corresponding to the characteristic browsing behavior according to the browsing content in the characteristic browsing behavior;
a frequency counting unit, configured to count browsing times of the category labels of the plurality of characteristic browsing behaviors, and when the browsing behavior record indicates that a content total ratio of the object user to the browsing content is non-zero, obtain the browsing times of the category labels according to the counting of the browsing times of the category labels by the content total ratio;
and the degree judging unit is used for counting the browsing frequency of the plurality of category labels according to the browsing frequency of the category labels, acquiring the browsing frequency of the category labels, setting the preference degree of the category labels according to the browsing frequency and the preset browsing frequency division, wherein the total browsing frequency of the category labels is 1, and the preference degree corresponds to the browsing frequency.
As a further scheme of the invention: the category labels comprise artificial labels and content labels;
the artificial tags are used for representing the category marks of the browsing contents by the object users, the number of the artificial tags is multiple, and when the artificial tags are used, the artificial tags with the required number in descending order sorting according to the marking times of the object users are sequentially selected;
and the content tag is used for representing the category mark of the browsing content by the object to be promoted.
As a further scheme of the invention: the effective browsing determination criterion is used for representing a criterion for determining the browsing completion degree of the browsing content by the object user, that is, when the completion degree of the browsing content by the object user is smaller than the effective browsing determination criterion, the open browsing behavior is determined to be invalid.
The embodiment of the invention aims to provide a user data analysis method, which comprises the following steps:
the method comprises the steps of obtaining the open browsing behavior of an object user, screening the open browsing behavior according to a preset effective browsing judgment standard, and obtaining a plurality of characteristic browsing behaviors, wherein the open browsing behavior is used for representing the browsing content of the object user and a browsing behavior record corresponding to the browsing content;
acquiring a plurality of category labels of the characteristic browsing behaviors, and performing browsing frequency statistics on the plurality of category labels according to a browsing behavior record to acquire the preference degrees of the object user on the plurality of category labels, wherein the category labels are used for representing the content categories of browsing contents corresponding to the characteristic browsing behaviors;
acquiring target browsing behaviors of the target user, acquiring a plurality of promotion labels of the target browsing behaviors, counting browsing frequency of the promotion labels, and acquiring the preference degrees of the target user on the plurality of promotion labels, wherein the target browsing behaviors are used for representing the public content of an object to be promoted, and the promotion labels are used for representing the content category of the public content;
and screening the promotion labels and the category labels according to a preset promotion standard, acquiring the promotion labels and the category labels and generating a guidance promotion scheme, wherein the guidance promotion scheme is used for promoting the public content corresponding to the promotion labels to the content corresponding to the category labels and browsing users.
As a further scheme of the invention: further comprising the step of screening and obtaining the object user:
a browsing user for acquiring the public content of the object to be promoted, wherein the browsing user is used for representing all users who browse the public content;
and acquiring the browsing behavior record of the browsing user on the public content, judging and screening according to the content total ratio of the browsing behavior record on the public content, and acquiring a plurality of object users, wherein the content total ratio is used for representing the browsing completion degree of the browsing user on a certain public content, and the object users are the browsing users with the content total ratio reaching a preset value.
As a further scheme of the invention: the step of obtaining a plurality of category labels of the characteristic browsing behavior, and performing statistics on browsing frequency of the plurality of category labels according to the browsing behavior record, and obtaining the preference degree of the object user for the plurality of category labels specifically includes:
acquiring the category label corresponding to the characteristic browsing behavior according to the browsing content in the characteristic browsing behavior;
counting the browsing times of the category labels of the characteristic browsing behaviors, and when the browsing behavior record shows that the content total ratio of the object user to the browsing content is not zero, counting the browsing times of the category labels according to the content total ratio to obtain the browsing times of the category labels;
and counting the browsing frequency of the plurality of category labels according to the browsing frequency of the category labels to obtain the browsing frequency of the category labels, wherein the sum of the browsing frequency of the plurality of category labels is 1, and setting preference degrees for the category labels according to the browsing frequency and preset browsing frequency division, wherein the preference degrees correspond to the browsing frequency.
Compared with the prior art, the invention has the beneficial effects that: through the arrangement of the user data acquisition module, the category label generation module, the label viscosity judgment module and the user guide generation module, the browsing records of the object users who browse the content published by the object to be promoted are analyzed, the browsing preference interest of the object users is judged, the browsing interest of the object users to the content published by the object users is judged, the corresponding published content of the user to be promoted is promoted to other users with the same similar browsing preference interest as the object users, the promotion purpose is achieved, compared with the existing promotion mode, more netted promotion is carried out based on the similarity among the users, and the promotion effect is more accurate and efficient.
Drawings
Fig. 1 is a block diagram of a user data analysis system.
FIG. 2 is a block diagram of a category label generation module in a user data analysis system.
Fig. 3 is a flow chart diagram of a user data analysis method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of specific embodiments of the present invention is provided in connection with specific embodiments.
As shown in fig. 1, a user data analysis system provided for an embodiment of the present invention includes:
the user data obtaining module 100 is configured to obtain a public browsing behavior of an object user, filter the public browsing behavior according to a preset effective browsing determination standard, and obtain a plurality of characteristic browsing behaviors, where the public browsing behavior is used to represent browsing content of the object user and a browsing behavior record corresponding to the browsing content.
The category label generating module 300 is configured to obtain a plurality of category labels of the characteristic browsing behavior, perform statistics on browsing frequency of the plurality of category labels according to a browsing behavior record, and obtain a preference degree of the object user for the plurality of category labels, where the category labels are used to represent content categories of browsing content corresponding to the characteristic browsing behavior.
The tag stickiness judgment module 500 is configured to obtain a target browsing behavior of the object user, obtain a plurality of promotion tags of the target browsing behavior, perform statistics on browsing frequency of the promotion tags, and obtain a degree of preference of the object user for the plurality of promotion tags, where the target browsing behavior is used to represent a public content of an object to be promoted, and the promotion tags are used to represent a category to which the public content belongs.
The user guidance generation module 700 is configured to filter the plurality of promotion tags and the category tags according to a preset promotion standard, obtain the plurality of promotion tags and the category tags, and generate a guidance promotion scheme, where the guidance promotion scheme is used to promote the public content corresponding to the promotion tags to the content corresponding to the category tags and browse a user.
In this embodiment, a user data analysis system is provided, which is used to analyze browsing records of an object user browsing contents published by an object to be promoted, determine browsing preference interests of the object user and browsing interests of the object user in self published contents, and further promote corresponding published contents of the object user to other users having similar browsing preference interests with the object user, so as to achieve the purpose of promotion, when in use, a user data acquisition module 100 acquires open browsing behaviors of the object user, and then analyzes and determines the open browsing behaviors through a category label generation module 300 to acquire a plurality of category labels, and then counts the category labels to acquire a preference degree, that is, a ratio of browsing times of the object user to contents related to the category labels (i.e. contents browsed more frequently), the tag stickiness judgment module 500 is configured to apply browsing preference of the content of the object user to be promoted to the published content of the user to be promoted, and according to the two data, the user guidance generation module 700 is configured to promote the browsing preference of the object user corresponding to the user to be promoted to other users having the same category tag preference degree, so as to form a more accurate purpose of increasing promotion for the user.
As another preferred embodiment of the present invention, the system further includes an object user filtering module, where the object user filtering module specifically includes:
and the user acquisition unit is used for acquiring browsing users of the public content of the object to be promoted, and the browsing users are used for representing all users who browse the public content.
And the user screening unit is used for acquiring the browsing behavior record of the browsing user on the public content, judging and screening the content total ratio of the public content according to the browsing behavior record, and acquiring a plurality of object users, wherein the content total ratio is used for representing the browsing completion degree of the browsing user on a certain public content, and the object users are the browsing users with the content total ratio reaching a preset value.
In this embodiment, the object user filtering module is configured to improve processing efficiency of the system when performing operations such as category tagging, because in practice, the content issued by the object to be promoted has a large number of browsing users, and a majority of the browsing behaviors are incomplete, for example, the user to be promoted issues a video of one minute, and in one thousand browsing records, half of the corresponding users all browse for less than thirty seconds, in this case, if the object to be promoted is determined according to the browsing behaviors, new users who may be introduced are also uninterested in the video content, and therefore need to be excluded.
As shown in fig. 2, as another preferred embodiment of the present invention, the category label generating module 300 includes:
a category obtaining unit 301, configured to obtain the category label corresponding to the characteristic browsing behavior according to the browsing content in the characteristic browsing behavior.
A frequency counting unit 302, configured to count browsing times of the category labels of the multiple characteristic browsing behaviors, and when the browsing behavior record indicates that the content total proportion of the browsing content by the object user is non-zero, obtain the browsing times of the category labels according to the counting of the browsing times of the category labels by the content total proportion.
A degree determining unit 303, configured to perform statistics on browsing frequencies of the plurality of category labels according to the browsing times of the category labels, to obtain the browsing frequency of the category labels, where a total browsing frequency of the plurality of category labels is 1, and set a preference degree for the category labels according to the browsing frequency and a preset browsing frequency partition, where the preference degree corresponds to the browsing frequency.
In this embodiment, the category label generating module 300 is functionally divided, and detailed conditions are described for the execution of some steps, and the statistics of the number of times of the content total amount accounting when the content total amount accounting is non-zero in the frequency statistics unit can be actually understood as: when the user repeatedly views the video for one minute, for example, 3.5 times (the completion of the three times of the whole plus one viewing is 50%), the number of views is counted as 3.5, and the viewing frequency can represent how frequently the user views the content of the tags of the plurality of categories, and thus can represent the preference degree.
As another preferred embodiment of the present invention, the category label includes an artificial label and a content label;
the artificial tags are used for representing the category marks of the browsing contents by the object users, the number of the artificial tags is multiple, and when the artificial tags are used, the artificial tags with the required number in descending order sorting according to the marking times of the object users are sequentially selected.
And the content tag is used for representing the category mark of the browsing content by the object to be promoted.
In this embodiment, the category labels are classified here, and the main reason is that the content labels set by the user to be promoted or set by the platform do not necessarily represent the perception and understanding of the content by the target user, so that the artificial labels marked by the user (other users may agree with the existing artificial labels, that is, the artificial labels are marked twice) are added to improve the accuracy of the labels in classifying the preference of the user.
As another preferred embodiment of the present invention, the valid browsing determination criterion is used to characterize a criterion for determining the browsing completion degree of the browsing content by the target user, that is, when the completion degree of the browsing content by the target user is smaller than the valid browsing determination criterion, the open browsing behavior is determined to be invalid.
As shown in fig. 3, the present invention further provides a user data analysis method, which includes the following steps:
s200, obtaining the open browsing behavior of the object user, screening the open browsing behavior according to a preset effective browsing judgment standard, and obtaining a plurality of characteristic browsing behaviors, wherein the open browsing behavior is used for representing the browsing content of the object user and the browsing behavior record corresponding to the browsing content.
S400, obtaining a plurality of category labels of the characteristic browsing behaviors, performing statistics on browsing frequency of the category labels according to browsing behavior records, and obtaining the preference degrees of the object user on the category labels, wherein the category labels are used for representing the content categories of the browsing contents corresponding to the characteristic browsing behaviors.
S600, acquiring target browsing behaviors of the object user, acquiring a plurality of promotion labels of the target browsing behaviors, counting browsing frequency of the promotion labels, and acquiring the preference degree of the object user to the plurality of promotion labels, wherein the target browsing behaviors are used for representing the public content of the object to be promoted, and the promotion labels are used for representing the content category of the public content.
S800, screening the promotion labels and the category labels according to a preset promotion standard, obtaining the promotion labels and the category labels and generating a guidance promotion scheme, wherein the guidance promotion scheme is used for promoting the public contents corresponding to the promotion labels to the contents corresponding to the category labels and browsing users.
As another preferred embodiment of the present invention, the method further includes the step of screening and acquiring the target user:
and acquiring a browsing user of the public content of the object to be promoted, wherein the browsing user is used for representing all users who browse the public content.
And acquiring the browsing behavior record of the browsing user on the public content, judging and screening according to the content total ratio of the browsing behavior record on the public content, and acquiring a plurality of object users, wherein the content total ratio is used for representing the browsing completion degree of the browsing user on a certain public content, and the object users are the browsing users with the content total ratio reaching a preset value.
As another preferred embodiment of the present invention, the step of acquiring a plurality of category labels of the characteristic browsing behaviors, and performing statistics on browsing frequency of the plurality of category labels according to the browsing behavior record, and acquiring the preference degrees of the target user for the plurality of category labels specifically includes:
and acquiring the category label corresponding to the characteristic browsing behavior according to the browsing content in the characteristic browsing behavior.
And counting the browsing times of the category labels of the characteristic browsing behaviors, and when the browsing behavior record shows that the content total ratio of the object user to the browsing content is not zero, counting the browsing times of the category labels according to the content total ratio to obtain the browsing times of the category labels.
And carrying out statistics on the browsing frequency of the plurality of category labels according to the browsing frequency of the category labels to obtain the browsing frequency of the category labels, wherein the sum of the browsing frequency of the plurality of category labels is 1, and setting preference degrees for the category labels according to the browsing frequency and preset browsing frequency division, wherein the preference degrees correspond to the browsing frequency.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a non-volatile computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. A user data analysis system, comprising:
the user data acquisition module is used for acquiring the open browsing behavior of an object user, screening the open browsing behavior according to a preset effective browsing judgment standard and acquiring a plurality of characteristic browsing behaviors, wherein the open browsing behavior is used for representing the browsing content of the object user and a browsing behavior record corresponding to the browsing content;
the category label generation module is used for acquiring a plurality of category labels of the characteristic browsing behaviors, counting the browsing frequency of the plurality of category labels according to a browsing behavior record, and acquiring the preference degree of the object user on the plurality of category labels, wherein the category labels are used for representing the content categories of the browsing content corresponding to the characteristic browsing behaviors;
the tag viscosity judging module is used for acquiring target browsing behaviors of the object user, acquiring a plurality of promotion tags of the target browsing behaviors, counting browsing frequency of the promotion tags, and acquiring the preference degree of the object user to the plurality of promotion tags, wherein the target browsing behaviors are used for representing the public content of an object to be promoted, and the promotion tags are used for representing the category of the public content;
and the user guidance generation module is used for screening the plurality of promotion labels and the category labels according to a preset promotion standard, acquiring the plurality of promotion labels and the category labels and generating a guidance promotion scheme, wherein the guidance promotion scheme is used for promoting the public contents corresponding to the promotion labels to the contents corresponding to the category labels and browsing users.
2. The system of claim 1, further comprising an object user filtering module, wherein the object user filtering module specifically comprises:
the user acquisition unit is used for acquiring browsing users of the public content of the object to be promoted, and the browsing users are used for representing all users who browse the public content;
and the user screening unit is used for acquiring the browsing behavior record of the browsing user on the public content, judging and screening the content total ratio of the public content according to the browsing behavior record, and acquiring a plurality of object users, wherein the content total ratio is used for representing the browsing completion degree of the browsing user on a certain public content, and the object users are the browsing users with the content total ratio reaching a preset value.
3. The system of claim 2, wherein the category label generating module comprises:
the category acquisition unit is used for acquiring the category label corresponding to the characteristic browsing behavior according to the browsing content in the characteristic browsing behavior;
a frequency counting unit, configured to count browsing times of the category labels of the plurality of characteristic browsing behaviors, and when the browsing behavior record indicates that a content total ratio of the object user to the browsing content is non-zero, obtain the browsing times of the category labels according to the counting of the browsing times of the category labels by the content total ratio;
and the degree judging unit is used for counting the browsing frequency of the plurality of category labels according to the browsing frequency of the category labels, acquiring the browsing frequency of the category labels, setting the preference degree of the category labels according to the browsing frequency and the preset browsing frequency division, wherein the total browsing frequency of the category labels is 1, and the preference degree corresponds to the browsing frequency.
4. The system of claim 1, wherein the category labels comprise an artifact label and a content label;
the artificial tags are used for representing the category marks of the browsing contents by the object users, the number of the artificial tags is multiple, and when the artificial tags are used, the artificial tags with the required number in descending order sorting according to the marking times of the object users are sequentially selected;
and the content tag is used for representing the category mark of the browsing content by the object to be promoted.
5. The system according to claim 1, wherein the valid browsing determination criterion is used to characterize a criterion for determining the browsing completion of the browsing contents by the target user, i.e. when the browsing completion of the browsing contents by the target user is smaller than the valid browsing determination criterion, the open browsing behavior is determined to be invalid.
6. A method for analyzing user data, comprising the steps of:
the method comprises the steps of obtaining the open browsing behavior of an object user, screening the open browsing behavior according to a preset effective browsing judgment standard, and obtaining a plurality of characteristic browsing behaviors, wherein the open browsing behavior is used for representing the browsing content of the object user and a browsing behavior record corresponding to the browsing content;
acquiring a plurality of category labels of the characteristic browsing behaviors, and performing browsing frequency statistics on the plurality of category labels according to a browsing behavior record to acquire the preference degrees of the object user on the plurality of category labels, wherein the category labels are used for representing the content categories of browsing contents corresponding to the characteristic browsing behaviors;
acquiring target browsing behaviors of the target user, acquiring a plurality of promotion labels of the target browsing behaviors, counting browsing frequency of the promotion labels, and acquiring the preference degrees of the target user on the plurality of promotion labels, wherein the target browsing behaviors are used for representing the public content of an object to be promoted, and the promotion labels are used for representing the content category of the public content;
and screening the promotion labels and the category labels according to a preset promotion standard, acquiring the promotion labels and the category labels and generating a guidance promotion scheme, wherein the guidance promotion scheme is used for promoting the public content corresponding to the promotion labels to the content corresponding to the category labels and browsing users.
7. The method according to claim 6, further comprising the step of screening the target user:
a browsing user for acquiring the public content of the object to be promoted, wherein the browsing user is used for representing all users who browse the public content;
and acquiring the browsing behavior record of the browsing user on the public content, judging and screening according to the content total ratio of the browsing behavior record on the public content, and acquiring a plurality of object users, wherein the content total ratio is used for representing the browsing completion degree of the browsing user on a certain public content, and the object users are the browsing users with the content total ratio reaching a preset value.
8. The method as claimed in claim 7, wherein the step of obtaining a plurality of category labels of the characteristic browsing behavior, and performing browsing frequency statistics on the plurality of category labels according to the browsing behavior record, and obtaining the preference degree of the target user for the plurality of category labels specifically includes:
acquiring the category label corresponding to the characteristic browsing behavior according to the browsing content in the characteristic browsing behavior;
counting the browsing times of the category labels of the characteristic browsing behaviors, and when the browsing behavior record shows that the content total ratio of the object user to the browsing content is not zero, counting the browsing times of the category labels according to the content total ratio to obtain the browsing times of the category labels;
and counting the browsing frequency of the plurality of category labels according to the browsing frequency of the category labels to obtain the browsing frequency of the category labels, wherein the sum of the browsing frequency of the plurality of category labels is 1, and setting preference degrees for the category labels according to the browsing frequency and preset browsing frequency division, wherein the preference degrees correspond to the browsing frequency.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116173513A (en) * | 2023-04-24 | 2023-05-30 | 深圳市乐易网络股份有限公司 | Intelligent game pushing system and method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10762190B1 (en) * | 2020-01-27 | 2020-09-01 | Capital One Services, Llc | Computer-based systems with programmed automatic real-time updatable browsing data objects and activity data objects and methods of use thereof |
| CN109325179B (en) * | 2018-09-17 | 2020-12-04 | 青岛海信网络科技股份有限公司 | Content promotion method and device |
| CN113901311A (en) * | 2021-09-30 | 2022-01-07 | 合肥小刺猬信息科技有限公司 | Data classification method and system based on machine learning |
-
2022
- 2022-07-07 CN CN202210791960.9A patent/CN114861074A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325179B (en) * | 2018-09-17 | 2020-12-04 | 青岛海信网络科技股份有限公司 | Content promotion method and device |
| US10762190B1 (en) * | 2020-01-27 | 2020-09-01 | Capital One Services, Llc | Computer-based systems with programmed automatic real-time updatable browsing data objects and activity data objects and methods of use thereof |
| CN113901311A (en) * | 2021-09-30 | 2022-01-07 | 合肥小刺猬信息科技有限公司 | Data classification method and system based on machine learning |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116173513A (en) * | 2023-04-24 | 2023-05-30 | 深圳市乐易网络股份有限公司 | Intelligent game pushing system and method |
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Application publication date: 20220805 |