CN113127683B - Content recommendation method, device, electronic equipment and medium - Google Patents
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- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
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Abstract
The disclosure discloses a content recommendation method, device, equipment, medium and product, and relates to the fields of big data, intelligent recommendation and the like. The content recommendation method comprises the following steps: comment data aiming at a target video is obtained, wherein the comment data is used for representing the attention degree of the target video; determining a target video segment from the target video based on the comment data, wherein the attention of the target video segment meets a preset attention condition; determining content to be recommended associated with comment data from at least one candidate content; and recommending the content to be recommended in response to detecting that the target video is played to the target video segment.
Description
Technical Field
The present disclosure relates to the field of computer technology, and in particular, to the fields of big data, intelligent recommendation, and the like, and more particularly, to a content recommendation method, a content recommendation apparatus, an electronic device, a medium, and a program product.
Background
In the related art, when a user is watching video, related content may be recommended to the user, for example, an advertisement may be recommended to the user. However, when recommending content, the related art does not relate to the content of the video, resulting in the recommended content being more abrupt. In addition, the related art does not make a recommendation based on the user's point of interest when recommending content, so that the recommended content is difficult to satisfy the user's needs.
Disclosure of Invention
The present disclosure provides a content recommendation method, apparatus, electronic device, storage medium, and program product.
According to an aspect of the present disclosure, there is provided a content recommendation method including: comment data aiming at a target video is obtained, wherein the comment data is used for representing the attention degree of the target video; determining a target video segment from the target video based on the evaluation data, wherein the attention of the target video segment meets a preset attention condition; determining content to be recommended associated with the comment data from at least one candidate content; and recommending the content to be recommended in response to detecting that the target video is played to the target video segment.
According to another aspect of the present disclosure, there is provided a content recommendation apparatus including: the system comprises an acquisition module, a first determination module, a second determination module and a recommendation module. The system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring comment data aiming at a target video, and the comment data is used for representing the attention degree of the target video; the first determining module is used for determining a target video segment from the target video based on the evaluation data, wherein the attention of the target video segment meets the preset attention condition; a second determining module, configured to determine content to be recommended associated with the comment data from at least one candidate content; and the recommending module is used for recommending the content to be recommended in response to detecting that the target video is played to the target video segment.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the content recommendation method described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the content recommendation method described above.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the content recommendation method described above.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
Fig. 1 schematically illustrates an application scenario of a content recommendation method and apparatus according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a content recommendation method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a content recommendation method according to another embodiment of the present disclosure;
FIG. 4 schematically illustrates a schematic diagram of a content recommendation method according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a content recommendation device according to an embodiment of the present disclosure; and
FIG. 6 is a block diagram of an electronic device for performing content recommendation to implement an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides a content recommendation method, which comprises the following steps: comment data for the target video is obtained, and the comment data are used for representing the attention degree of the target video. Then, based on comment data, determining a target video segment from the target video, wherein the attention of the target video segment meets the preset attention condition. Next, content to be recommended associated with the comment data is determined from the at least one candidate content, and the content to be recommended is recommended in response to detecting that the target video is played to the target video clip.
Fig. 1 schematically illustrates an application scenario of a content recommendation method and apparatus according to an embodiment of the present disclosure. It should be noted that fig. 1 illustrates only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments, or scenarios.
As shown in fig. 1, the application scenario 100 according to this embodiment may include a server 100A, a client 100B, and a network 100C. Network 100C is the medium used to provide communication links between server 100A and client 100B. Network 100C may include various connection types such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with server 100A over network 100C using client 100B to receive or send messages, etc. The client 100B may have installed thereon various communication client applications such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, and the like (by way of example only).
For example, client 100B may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like. The client 100B of the embodiment of the present disclosure may play video, for example.
The server 100A may be a server providing various services, such as a background management server (by way of example only) that provides support for websites that users browse with the client 100B. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the client. In addition, the server 100A may also be a cloud server, that is, the server 100A has a cloud computing function.
Illustratively, the user may comment on the target video 101 while watching the target video 101 played by the client 100B. Client 100B may store comment data of the user. The comment data may include, for example, first-type comment data 102 and second-type comment data 103.
The first-type comment data 102 is, for example, data shown in a comment area, and the first-type comment data 102 generally requires a user to actively go to the comment area for browsing. The second type of comment data 103 is comment data transmitted by a bullet screen, for example, and the second type of comment data 103 is actively displayed in a display area of the target video 101, for example, and a user viewing the target video 101 can passively receive the second type of comment data 103. The association between the second comment data 103 and the target video 101 is strong.
Server 100A may obtain comment data for the user from at least one client, including client 100B. The server 100A then processes the comment data to determine the content 104 to be recommended that is associated with the comment data. The comment data acquired by the server 100A is, for example, history data, that is, comment data transmitted when a plurality of users previously viewed the target video 101.
For example, after the server 100A acquires comment data, the content 104 to be recommended associated with the comment data may be selected from the database, and the content 104 to be recommended may be advertisement content, for example. When the subsequent server 100A detects that the client 100B plays the target video 101 again, the server 100A may recommend the content 104 to be recommended to the client 100B, so that the client 100B displays the content 104 to be recommended, which is implemented as the user recommending the content 104 to be recommended.
It should be noted that, the content recommendation method provided by the embodiment of the present disclosure may be performed by the server 100A. Accordingly, the content recommendation apparatus provided by the embodiments of the present disclosure may be provided in the server 100A. The content recommendation method provided by the embodiments of the present disclosure may also be performed by a server or server cluster other than the server 100A and capable of communicating with the client 100B and/or the server 100A. Accordingly, the content recommendation device provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 100A and is capable of communicating with the client 100B and/or the server 100A.
It should be understood that the number of clients, networks, and servers in fig. 1 is merely illustrative. There may be any number of clients, networks, and servers, as desired for implementation.
The embodiment of the present disclosure provides a content recommendation method, and a content recommendation method according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2 to 4 in conjunction with the application scenario of fig. 1. The content recommendation method of the embodiment of the present disclosure may be performed by the server 100A shown in fig. 1, for example.
Fig. 2 schematically illustrates a flowchart of a content recommendation method according to an embodiment of the present disclosure.
As shown in fig. 2, the content recommendation method 200 of the embodiment of the present disclosure may include, for example, operations S210 to S240.
In operation S210, comment data for a target video is acquired.
In operation S220, a target video clip is determined from the target video based on the comment data.
In operation S230, a content to be recommended associated with comment data is determined from among at least one candidate content.
In response to detecting that the target video is played to the target video clip, the content to be recommended is recommended in operation S240.
For example, comment data for a target video may be used to characterize the focus of the target video. The greater the number of comment data for the target video, the higher the degree of attention that represents the target video. After comment data for the target video is acquired, a target video segment whose attention degree satisfies a preset attention degree condition may be determined from the target video based on the comment data, and the determined attention degree of the target video segment is higher, for example.
Next, the content to be recommended associated with the comment data may be determined from among a plurality of candidate contents stored in the database. For example, the subject matter of the content to be recommended is consistent with the subject matter of the comment data, or related information of the comment data is contained in the content to be recommended.
Because the attention of the target video segment is higher, when the target video is played again later, the content to be recommended can be recommended when the target video segment is detected to be played, so that the content to be recommended has higher attention.
According to the embodiment of the disclosure, the target video clip with high attention is determined by acquiring comment data for the target video and based on the comment data. And when the target video is played again later, recommending the content to be recommended which is associated with the comment data when the playing to the target video segment is detected. It can be understood that, as the comment data embody the content of the target video to a certain extent, the content to be recommended associated with the comment data is recommended, and the association degree between the content to be recommended and the content of the target video is improved. In addition, as the attention of the target video segment is higher, the content to be recommended is recommended when the target video segment is played, so that the content to be recommended has higher attention.
Fig. 3 schematically illustrates a flow chart of a content recommendation method according to another embodiment of the present disclosure.
As shown in fig. 3, the content recommendation method 300 of the embodiment of the present disclosure may include, for example, operations S301 to S309.
In operation S301, comment data for a target video is acquired.
In operation S302, a plurality of video clips are determined from a target video.
In operation S303, a target video clip is determined from a plurality of video clips for the target video based on the comment data.
In one embodiment, the target video may be divided into a plurality of video segments that do not overlap. For example, when the duration of the target video is 10 minutes, the target video is divided into 20 video clips, each of which has a duration of 30 seconds, and the contents of each video clip do not overlap with each other.
In another example, multiple video clips may be extracted from the target video, and any two of the multiple video clips may or may not have overlapping content. For example, when the duration of the target video is 10 minutes, the video clip of the first 30 seconds is extracted as the first video clip, the video clips of the 6 th to 35 th seconds are extracted as the second video clip, the video clips of the 11 th to 40 th seconds are extracted as the third video clip, and so on, to obtain a plurality of video clips. Wherein any two video clips of the plurality of video clips may have overlapping content, for example, the second video clip and the third video clip have overlapping content of 11 th to 35 th seconds.
After a plurality of video clips are determined from the target video, a video clip whose attention satisfies a preset attention condition may be determined from the plurality of video clips as the target video clip.
In an embodiment, the target video clips having a focus degree satisfying the preset focus degree condition, for example, include a number of comment data for the target video clips greater than a preset number. That is, the comment data acquired is for the target video, with each video clip corresponding to a corresponding number of comment data. One or more video clips with the number of comment data larger than the preset number can be determined from the video clips based on the number of comment data corresponding to each video clip and used as target video clips.
In another embodiment, the number of comment data for the target video clip is greater than the number of comment data for the remaining video clip, the remaining video clip being a video clip of the plurality of video clips other than the target video clip. That is, one or more video clips having the largest number of comment data among the plurality of video clips are determined as the target video clip.
In operation S304, candidate comment data for the target video clip is determined from the plurality of comment data.
In operation S305, the plurality of candidate comment data is classified to obtain a plurality of categories.
In operation S306, a target category is determined from among the plurality of categories based on the number of candidate comment data in each category.
In operation S307, content to be recommended is determined from at least one candidate content based on the target category.
The comment data for the target video includes, for example, a plurality of comment data. Each comment data includes, for example, a comment time, from which it can be determined for which video clip each comment data is for. The comment time includes, for example, a comment time when the comment time of a certain comment data is within a range of the duration of a certain video clip, indicating that the comment data is data for the video clip.
A plurality of candidate comment data for the target video clip is extracted from the plurality of comment data based on the comment time of each comment data and the time length information of each video clip. And then, classifying the plurality of candidate comment data according to the content of each candidate comment data to obtain a plurality of categories. The plurality of candidate comment data may be classified using, for example, a classification model.
Each category includes, for example, at least one candidate comment data, and candidate comment data belonging to the same category are similar to each other. For example, a plurality of candidate comment data belonging to a certain category are all data related to food.
Next, the category having the largest number of candidate comment data is set as the target category among the plurality of categories. And determining the content to be recommended associated with the candidate comment data from a plurality of candidate contents based on a fuzzy matching algorithm aiming at the candidate comment data included in the target category.
For example, the plurality of candidate content includes a first candidate content related to food, a second candidate content related to sports, a third candidate content related to travel. And when the candidate comment data in the target category are all food related data, determining first candidate content associated with the target category as content to be recommended based on a fuzzy matching algorithm.
In another example, key information, such as keywords, may also be extracted from candidate comment data for the target category. For example, when the candidate comment data in the target category are all data related to a food, the extracted key information is, for example, "XX beverage". Next, candidate content including key information is selected from at least one candidate content as content to be recommended. For example, candidate content including "XX drink" is selected from a plurality of candidate contents as the content to be recommended.
In an embodiment of the present disclosure, after a plurality of candidate comment data for a target video clip is acquired, the candidate comment data is further classified to determine a target category with the largest number of candidate comment data. Because the attention of the target video segment is higher and the candidate comment data in the target category characterizes the main attention point aiming at the target video segment, the attention of the associated content to be recommended is improved by determining the associated content to be recommended based on the candidate comment data in the target category.
In operation S308, it is detected whether the target video is played to the target video clip. If so, operation S309 is performed. If not, operation S308 is performed in return.
In response to detecting that the target video is played to the target video clip, the content to be recommended is recommended in operation S309.
And when the target video clips and the contents to be recommended are determined, and then the target video is played again, detecting whether the target video is played to the target video clips or not in real time. If the target video is played to the target video clip, recommending the content to be recommended.
According to the embodiment of the disclosure, the target video clip with high attention is determined by acquiring comment data for the target video and based on the comment data. Then, content to be recommended associated with the target video clip is determined based on the candidate comment data for the target video clip. And when the target video is played again later, recommending the content to be recommended which is associated with the comment data when the playing to the target video segment is detected. It can be understood that, as the comment data embody the content of the target video to a certain extent, the candidate comment data for the target video segment with high attention is used for determining the content to be recommended, so that the association degree of the content to be recommended and the target video segment is improved, and the attention of the content to be recommended is higher. In addition, as the attention degree of the target video segment is higher, the content to be recommended is recommended when the target video segment is played, so that the attention degree of the content to be recommended is further improved, and the content to be recommended meets the requirements of users.
Fig. 4 schematically illustrates a schematic diagram of a content recommendation method according to an embodiment of the present disclosure.
As shown in fig. 4, the target video 401 has a plurality of video clips, for example, a video clip 401A, a video clip 401B, and a video clip 401C are described as an example. The comment data for the target video includes, for example, comment data for each video clip. For example, comment data for a target video includes a plurality of comment data 402A for a video clip 401A, a plurality of comment data 402B for a video clip 401B, and a plurality of comment data 402C for a video clip 401C.
Based on the number of comment data, a target video clip is determined from the plurality of video clips 401A, 401B, 401C. For example, the number of the plurality of comment data 402B is larger than the number of the plurality of comment data 402A, the number of the plurality of comment data 402B is larger than the number of the plurality of comment data 402C, and the video clip 401B corresponding to the plurality of comment data 402B can be determined as the target video clip.
Next, a plurality of comment data 402B for a video clip 401B (target video clip) are divided as candidate comment data, resulting in a plurality of categories 403A, 403B, 403C, each including at least one candidate comment data. One or more categories with the largest candidate comment data are set as target categories, for example, category 403B is set as target category. Then, based on the candidate comment data in the category 403B (target category), the content to be recommended 405 is determined from the plurality of candidate contents 404. The content to be recommended 405 is associated with candidate comment data in a category 403B (target category).
By way of example, candidate content is, for example, advertising content including, for example, videos, pictures, icons, text, and the like. Advertisement content associated with candidate comment data in the target category is selected from among a plurality of advertisement content to recommend the advertisement content.
For example, taking a target video as a food video, when a host in the food video starts to eat, the host publicly takes off glasses, and takes the action of taking the glasses and preparing to eat as a feature of the host. When a video is played to a video clip (target video clip) ready for eating by taking off the glasses, many watching users initiate a lot of comments to tune. By acquiring comment data, it is possible to know that the video clip for taking a meal is prepared for taking a pair of glasses, and the comment amount of the user is large. At this time, comment data for a video clip that is ready to eat for picking up glasses may be taken as candidate comment data. Then, the candidate comment data is classified to obtain a plurality of categories, and the comment data about "glasses" in the plurality of categories is found to be large, and the category for "glasses" is set as the target category. Then, based on the target category, the content to be recommended associated with "glasses" is determined from the plurality of candidate contents to be recommended. The content to be recommended includes, for example, advertisement content including, for example, videos, pictures, icons, characters, and the like associated with "glasses". For example, "glasses+extraction" is extracted from candidate comment data in a target category as key information, and the key information is matched with candidate contents stored in a database to obtain contents to be recommended, and the contents to be recommended obtained by matching are, for example, "XXX laser for myopia treatment", "glasses are not taken out to be dream", and the like. When the food video is played again later, when the video is played to the video clip for picking glasses and preparing for eating, XXX laser can be recommended for treating myopia, and the glasses are not taken off for dream.
It can be appreciated that, compared to recommending pre-post advertisements, pause advertisements, post-post advertisements when video is played, the content recommendation scheme of the embodiments of the present disclosure fully considers the content of the video and comment data of the user, so that the recommended content (advertisement) is associated with the video content and meets the needs of the user.
Illustratively, a pre-post ad is, for example, an ad that is recommended prior to video playback, a pause ad is, for example, an ad that is recommended while the video is paused, and a post-post ad is, for example, an ad that is recommended after video playback is completed.
Fig. 5 schematically illustrates a block diagram of a content recommendation device according to an embodiment of the present disclosure.
As shown in fig. 5, the content recommendation apparatus 500 of the embodiment of the present disclosure includes, for example, an acquisition module 510, a first determination module 520, a second determination module 530, and a recommendation module 540.
The acquisition module 510 may be configured to acquire comment data for a target video, where the comment data is used to characterize a degree of interest of the target video. According to an embodiment of the present disclosure, the obtaining module 510 may perform, for example, operation S210 described above with reference to fig. 2, which is not described herein.
The first determining module 520 may be configured to determine a target video segment from the target video based on the comment data, where a degree of interest of the target video segment meets a preset degree of interest condition. According to an embodiment of the present disclosure, the first determining module 520 may perform, for example, the operation S220 described above with reference to fig. 2, which is not described herein.
The second determination module 530 may be configured to determine content to be recommended associated with comment data from at least one candidate content. The second determining module 530 may, for example, perform operation S230 described above with reference to fig. 2, which is not described herein.
The recommendation module 540 may be configured to recommend content to be recommended in response to detecting that the target video is played to the target video clip. According to an embodiment of the present disclosure, the recommendation module 540 may perform, for example, the operation S240 described above with reference to fig. 2, which is not described herein.
According to an embodiment of the present disclosure, the comment data includes a plurality of comment data; the second determination module 530 includes a first determination sub-module and a second determination sub-module. And the first determination submodule is used for determining candidate comment data from a plurality of comment data, wherein the candidate comment data are data aiming at the target video clips. And the second determining submodule is used for determining the content to be recommended which is associated with the candidate comment data from at least one candidate content.
According to an embodiment of the present disclosure, the candidate comment data includes a plurality of candidate comment data; the second determination submodule comprises a classification unit, a first determination unit and a second determination unit. And the classification unit is used for classifying the plurality of candidate comment data to obtain a plurality of categories. A first determination unit configured to determine a target category from among the plurality of categories based on the number of candidate comment data in each category. And the second determining unit is used for determining the content to be recommended from at least one candidate content based on the target category, wherein the content to be recommended is associated with the candidate comment data in the target category.
According to an embodiment of the present disclosure, the second determining unit includes: the extraction subunit and the selection subunit. And the extraction subunit is used for extracting key information from the candidate comment data of the target category. And the selecting subunit is used for selecting candidate contents containing key information from at least one candidate content as contents to be recommended.
According to an embodiment of the present disclosure, the satisfaction of the preset attention condition by the attention of the target video clip includes at least one of: the number of comment data aiming at the target video clips is larger than the preset number; and the number of comment data for the target video clip is greater than the number of comment data for the remaining video clips, wherein the remaining video clips are video clips in the target video other than the target video clip.
According to an embodiment of the present disclosure, the content to be recommended includes advertisement content.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
FIG. 6 is a block diagram of an electronic device for performing content recommendation to implement an embodiment of the present disclosure.
Fig. 6 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. The electronic device 600 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the respective methods and processes described above, such as a content recommendation method. For example, in some embodiments, the content recommendation method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the content recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the content recommendation method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (9)
1. A content recommendation method, comprising:
Comment data aiming at a target video is obtained, wherein the comment data is used for representing the attention degree of the target video;
Dividing a target video into a plurality of video clips, and determining a video frequency band with the maximum comment data amount from the plurality of video clips as the target video clip;
determining a plurality of comment data for the target video clip as candidate comment data;
classifying the candidate comment data to obtain a plurality of categories;
Taking the category with the largest number of candidate comment data in the categories as a target category, wherein the target category corresponds to the most dominant attention point;
Determining content to be recommended associated with candidate comment data in the target category from at least one candidate content based on the target category; and
And recommending the content to be recommended in response to detecting that the target video is played to the target video segment.
2. The method of claim 1, wherein the determining, based on the target category, content to be recommended associated with candidate comment data in the target category from at least one candidate content comprises:
extracting key information from candidate comment data of the target category; and
And selecting candidate contents containing the key information from the at least one candidate content as the contents to be recommended.
3. The method of claim 1 or 2, wherein the content to be recommended comprises advertising content.
4. A content recommendation device, comprising:
The system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring comment data aiming at a target video, and the comment data is used for representing the attention degree of the target video;
The target video segment determining module is used for dividing a target video into a plurality of video segments, and determining the video frequency band with the maximum comment data amount from the plurality of video segments as the target video segment;
The candidate comment data determining module is used for determining a plurality of comment data aiming at the target video clip as candidate comment data;
the classification module is used for classifying the candidate comment data to obtain a plurality of categories;
The target category determining module is used for taking the category with the largest number of candidate comment data in the categories as a target category, wherein the target category corresponds to the most main focus;
The content to be recommended determining module is used for determining content to be recommended, which is associated with candidate comment data of the target category, from at least one candidate content based on the target category; and
And the recommending module is used for recommending the content to be recommended in response to detecting that the target video is played to the target video segment.
5. The apparatus of claim 4, wherein the content to be recommended determination module comprises:
An extracting unit, configured to extract key information from candidate comment data of the target category; and
And the selection unit is used for selecting the candidate content containing the key information from the at least one candidate content as the content to be recommended.
6. The apparatus of claim 4 or 5, wherein the content to be recommended comprises advertising content.
7. An electronic device, comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-3.
8. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-3.
9. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-3.
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