CN114036342A - Video recommendation method, device and equipment and readable storage medium - Google Patents
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Abstract
The application discloses a video recommendation method, a device, equipment and a readable storage medium, wherein the method comprises the following steps: acquiring real-time behavior data and a scene of a user; if the historical portrait corresponding to the user exists, updating the historical portrait by using the real-time behavior data and the scene to obtain the current portrait of the user; acquiring a video recommendation set matched with the current portrait; pushing the video recommendation set to the user. Obviously, the historical portrait of the user is updated by real-time user behavior data and the scene where the user is located, the obtained current portrait of the user can contain the current interest intention of the user and cannot be too much different from the historical portrait of the user, and then a video recommendation set matched with the current portrait of the user can be accurately obtained and pushed to the user, so that the effects of increasing the user viscosity and improving the user retention rate are achieved.
Description
Technical Field
The present application relates to the field of video recommendation technologies, and in particular, to a video recommendation method, apparatus, device, and readable storage medium.
Background
In recent years, the short video industry is developed vigorously, more and more video resources are provided, and a video platform faces a problem, namely how to screen out videos which meet the user interest from massive video resources.
Most video platforms at present generally adopt the method that a user portrait is constructed according to historical data of a user, and then videos recommended to the user are determined according to the user portrait. However, the conventional video recommendation method has a significant problem that the update period of the portrait of the user is too long and cannot necessarily reflect the current intention of the user, which may cause the video recommended to the user to deviate from the current interest of the user.
Therefore, how to accurately recommend videos meeting the current interest of the user to increase the stickiness of the user is a considerable problem.
Disclosure of Invention
In view of the above, the present application provides a video recommendation method, apparatus, device and readable storage medium, which are used for accurately recommending videos that meet the current interests of users, so as to increase the user stickiness.
In order to achieve the above object, the following solutions are proposed:
a video recommendation method, comprising:
acquiring real-time behavior data and a scene of a user;
if the historical portrait corresponding to the user exists, updating the historical portrait by using the real-time behavior data and the scene to obtain the current portrait of the user;
acquiring a video recommendation set matched with the current portrait;
pushing the video recommendation set to the user.
Preferably, the video recommendation method further includes:
and if the historical portrait corresponding to the user does not exist, pushing a preset video recommendation set to the user.
Preferably, the obtaining a video recommendation set matching the current portrait includes:
and acquiring recommended videos with the target quantity matched with the current portrait to form a video recommendation set, wherein the target quantity is a preset video recommendation quantity.
Preferably, the obtaining a video recommendation set matching the current portrait further includes:
determining a video recall strategy according to the current portrait;
and acquiring a video recommendation set matched with the video recall strategy.
Preferably, the obtaining a video recommendation set matching the video recall policy includes:
acquiring a candidate video recommendation set matched with the video recall strategy;
and editing the recommended videos in the candidate video set and sequencing the recommended videos according to preset service interference and a video display environment to obtain a video recommended set, wherein the editing comprises adding and/or deleting.
Preferably, before the pushing the video recommendation set to the user, the method further comprises:
and adding preset display information to the recommended videos in the video recommendation set.
Preferably, the historical representation creating process includes:
acquiring behavior data of the user in a set period;
and updating the historical portrait of the user by using the behavior data to obtain an updated historical portrait.
A video recommendation apparatus comprising:
the data acquisition unit is used for acquiring real-time behavior data of a user and a scene where the user is located;
the portrait updating unit is used for updating the historical portrait by using the real-time behavior data and a scene to obtain the current portrait of the user if the historical portrait corresponding to the user exists;
the video set acquisition unit is used for acquiring a video recommendation set matched with the current portrait;
and the video set pushing unit is used for pushing the video recommendation set to the user.
A video recommendation device comprising a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the video recommendation method.
A readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the above-mentioned video recommendation method.
According to the scheme, the video recommendation method provided by the application comprises the following steps: acquiring real-time behavior data and a scene of a user; if the historical portrait corresponding to the user exists, updating the historical portrait by using the real-time behavior data and the scene to obtain the current portrait of the user; acquiring a video recommendation set matched with the current portrait; pushing the video recommendation set to the user. Obviously, the historical portrait of the user is updated by real-time user behavior data and the scene where the user is located, the obtained current portrait of the user can contain the current interest intention of the user and cannot be too much different from the historical portrait of the user, and then a video recommendation set matched with the current portrait of the user can be accurately obtained and pushed to the user, so that the effects of increasing the user viscosity and improving the user retention rate are achieved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart of a video recommendation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a video recommendation apparatus according to an embodiment of the present application;
fig. 3 is a block diagram of a hardware structure of a video recommendation device disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a video recommendation method provided in an embodiment of the present application, where the method includes:
step S100: acquiring real-time behavior data and a scene of a user;
specifically, the behavior data of the user may be data generated by an operation behavior of the user on the video page, where the operation behavior includes multiple behaviors, such as: browsing situation, praise, comment, viewing duration, sharing, and the like.
In addition, the scene where the user is located may be determined according to various information, such as: according to the current region where the user is located, the current time, other users concerned by the user, the video category according with the user intention and the like.
Step S110: and if the historical portrait corresponding to the user exists, updating the historical portrait by using the real-time behavior data and the scene to obtain the current portrait of the user.
Specifically, whether the historical portrait of the user exists or not can be inquired, and if the historical portrait of the user exists, the historical portrait of the user can be updated by using the real-time behavior data and the scene obtained in the steps, so that the current portrait of the user can accord with the current browsing intention of the user.
The historical representation of the user may include a number of characteristics of the user, such as: personal information, interest characteristics, relationship characteristics with other users, and the like, wherein the interest characteristics may include long-term interest characteristics, medium-term interest characteristics, and the like.
Step S120: and acquiring a video recommendation set matched with the current portrait.
Specifically, a video recommendation set matching the current portrait of the user may be obtained in the video repository. Since the current portrait of the user may include a plurality of features, the acquired video recommendation set may include a plurality of types of videos corresponding to the plurality of features.
Step S130: pushing the video recommendation set to the user.
Specifically, a video recommendation set may be pushed to the terminal of the user.
According to the scheme, the historical portrait of the user is updated by using real-time user behavior data and the scene where the user is located, the obtained current portrait of the user can contain the current interest intention of the user and cannot be too much different from the historical portrait of the user, and then the video recommendation set matched with the current portrait of the user can be accurately obtained and pushed to the user, so that the effects of increasing the user viscosity and improving the user retention rate are achieved.
Considering that some users are newly registered users, and therefore there is no user image constructed according to the behavior data of the new user, for such users without corresponding historical images, the embodiment of the present application may further include the following processes:
and if the historical portrait corresponding to the user does not exist, pushing a preset video recommendation set to the user.
Specifically, the preset video recommendation set may include multiple types of videos, such as: hot videos, recent videos, social events videos, and the like.
Furthermore, after the user browses the pushed preset video, the initial portrait of the user can be obtained, the video matched with the initial portrait can be pushed to the user, then the portrait of the user can be continuously updated according to the behavior data of the user, and the pushed video is ensured to be in line with the intention of the user.
According to the scheme, after the process is added, a closed-loop video recommendation method, an autonomous learning and continuous optimization method can be formed, and videos which are more and more accurately pushed and meet the interest intention of the user can be guaranteed.
In some embodiments of the present application, the process of obtaining the video recommendation set matching the current portrait in step S120 is introduced, and the process will be further described below.
Specifically, the process may include:
and acquiring recommended videos with the target quantity matched with the current portrait to form a video recommendation set, wherein the target quantity is a preset video recommendation quantity.
To avoid that the user browses the same type of video for a long time, the number of recommended videos in the video recommendation set may vary, examples are: different amounts of recommended videos are obtained for the same user representation at different time periods.
According to the scheme, after a certain number of recommended videos are obtained, the user portrait can be updated again according to behavior data of the user when the user browses all the recommended videos, and then a new video recommendation set can be obtained and pushed, so that videos browsed by the user cannot be single and tedious and are in line with interests of the user.
In some embodiments of the present application, the process of obtaining the video recommendation set matching the current portrait in step S120 is introduced, and the process of obtaining the video recommendation set will be further described below.
Specifically, the process of obtaining the video recommendation set may include:
and S1, determining a video recall strategy according to the current portrait.
In particular, the current representation may include a plurality of user characteristics, each of which may correspond to a different video recall policy, such that a video recall policy corresponding to a user characteristic included in the current representation may be determined. Different types of videos can be acquired by different video recall strategies.
The above-mentioned obtaining of the candidate video recommendation set by using the recall policy may be regarded as a preliminary screening of the video recommendation set, and the recalled recommended video may be used as a ranked candidate video recommendation set, so as to directly determine whether the recommended video participating in the ranking meets the user intention interest.
Each recall policy may be a video recommendation service, and the use of the recall policy mainly considers some problems, such as: the method comprises the steps of screening videos in a video library by different algorithms and rules, and reducing the number of videos participating in sequencing on the premise of ensuring high quality of the videos.
Different user characteristics in the user representation may establish different recall policies, examples being: recommending based on recent interest of users, recommending based on long-term interest of users, recommending based on regions of users, recommending based on last program of users in association, recommending based on new heat of users, recommending based on user differentiation, recommending based on user relationship and the like
And S2, acquiring a video recommendation set matched with the video recall strategy.
Specifically, multiple videos matching the video recall policy may be obtained and combined into a video recommendation set.
According to the scheme, the user characteristics contained in the current portrait of the user are determined, and the video recall strategy can be rapidly determined according to the user characteristics, so that the video which meets the current interest intention of the user can be obtained.
The above procedure for obtaining a video recommendation set will be described next with specific examples.
Specifically, the interest characteristics of the user included in the current representation of the user may be taken as an example.
The target video tag and the target video score corresponding to the interest feature can be determined according to the preset relation between the interest feature of the user and the video tag and the video score, then the scene feature contained in the current portrait of the user can be determined, the scene feature can be in the same region range as the user, a video recall strategy is determined according to the scene feature, and a recommended video matched with the target video tag and the target video score under the video recall strategy is obtained.
The scene of the obtained recommended video is in the same region range with the user, so that the browsing probability of the user can be improved, the click rate and the watching duration of the user can be increased, and the retention rate of the user can be improved.
In some embodiments of the present application, the process of obtaining the video recommendation set matching the video recall policy in step S2 is introduced, and the process will be further described below.
Specifically, the process may include:
and S21, acquiring a candidate video recommendation set matched with the video recall strategy.
Specifically, each video recall strategy can acquire a plurality of recommended videos, and the recommended videos at this time can be used as candidate video recommendation sets and can be determined as final video recommendation sets after being processed in the next step.
And S22, editing the recommended videos in the candidate video set and sequencing the videos according to preset service interference and video display environments to obtain a video recommended set, wherein the editing comprises adding and/or deleting.
Specifically, not all the obtained recommended videos in the candidate video recommendation set are suitable for being pushed to the user, so that the videos in the candidate video recommendation set can be edited, for example: the obtained recommended videos are videos of people, but the people of one video are not suitable for pushing the video of the people to a client due to recent changes, and the video can be deleted at the moment; as another example, recently occurring current hotspots, although not necessarily matching recall strategies in terms of their basis, may be added to the candidate video recommendation set in order for the user to see such videos.
After the videos are deleted or added, the candidate video recommendation sets can be ranked, and therefore a user can obtain better browsing experience when browsing the recommended videos.
According to the scheme, after the candidate video recommendation set is acquired, the candidate recommended videos can be added or deleted, and the method is not limited to an acquisition mode, so that the acquisition mode of the recommended videos is more flexible.
In order to allow the user to quickly determine the content of each recommended video when receiving the video recommendation set, a process of adding video information may be added before pushing the video recommendation set to the user in step S130.
Specifically, the process may include:
and adding preset display information to the recommended videos in the video recommendation set.
Specifically, the presentation information may be profile information of the recommended video, or other user's evaluation of the recommended video content.
In addition, the position for adding the presentation information may be a brief case or a cover page of the recommended video, or other optional positions, which are not strictly limited herein.
According to the scheme, the key information of the recommended video can be displayed by adding the display information, the attention of the user can be attracted, the recommended video can be browsed, and the user terminal displaying the recommended video can directly display the recommended video without adding a process for acquiring the display information.
In some embodiments of the present application, a process of step S110, if there is a historical portrait corresponding to the user, updating the historical portrait by using the real-time behavior data and a scene to obtain a current portrait of the user is introduced, and a process of creating a historical portrait of the user is introduced next.
Specifically, the process may include the steps of:
and S1, acquiring the behavior data of the user in a set period.
Specifically, the behavior data of the user in the set period may be obtained periodically, and the behavior data may be consistent with the operation behavior of the user on the video page mentioned in the foregoing step, which is not described herein again.
S2, using the behavior data to update the historical portrait of the user, and obtaining the updated historical portrait.
Specifically, the user characteristics included in the user behavior data may be extracted, and the historical portrait data of the user may be updated with the extracted user behavior data to obtain a new historical portrait in the period.
In addition, the historical representation of the user may also contain basic data of the user, such as: personal information of the user, such as gender, age, address, terminal equipment when the user browses videos, traffic packages used by the user when browsing videos, and the like.
According to the scheme, the historical portrait of the user is updated regularly, when a video pushing request is made to the user, the new historical portrait is updated according to the real-time behavior data of the user, so that the more accurate current portrait of the user can be obtained, and the video meeting the interest intention of the user can be recommended to the user more accurately.
The following describes a video recommendation device provided in an embodiment of the present application, and the video recommendation device described below and the video recommendation device described above may be referred to correspondingly.
First, referring to fig. 2, a video recommendation apparatus is described, and as shown in fig. 2, the video recommendation apparatus may include:
the data acquisition unit 100 is used for acquiring real-time behavior data of a user and a scene where the user is located;
a portrait updating unit 110, configured to update the historical portrait by using the real-time behavior data and a scene to obtain a current portrait of the user if the historical portrait corresponding to the user exists;
a video set obtaining unit 120, configured to obtain a video recommendation set matching the current portrait;
a video set pushing unit 130, configured to push the video recommendation set to the user.
Optionally, the video recommendation apparatus may further include:
and the preset video set pushing unit is used for pushing a preset video recommendation set to the user if the historical portrait corresponding to the user does not exist.
Optionally, the video set obtaining unit 120 may include:
and the target number video set acquisition unit is used for acquiring recommended videos of a target number matched with the current portrait to form a video recommendation set, wherein the target number is a preset video recommendation number.
Optionally, the video set obtaining unit 120 may further include:
the recall strategy determining unit is used for determining a video recall strategy according to the current portrait;
and the target video set acquisition unit is used for acquiring a video recommendation set matched with the video recall strategy.
Optionally, the target video set obtaining unit may include:
the candidate video set acquisition unit is used for acquiring a candidate video recommendation set matched with the video recall strategy;
and the video set editing unit is used for editing the recommended videos in the candidate video set and sequencing the recommended videos according to preset service interference and video display environments to obtain a video recommended set, wherein the editing comprises addition and/or deletion.
Optionally, the video recommendation apparatus may further include:
and the information adding unit is used for adding preset display information to the recommended videos in the video recommendation set.
Optionally, the video recommendation apparatus may include a historical representation creating unit, where the historical representation creating unit may include:
the period data acquisition unit is used for acquiring the behavior data of the user in a set period;
and the periodic portrait updating unit is used for updating the historical portrait of the user by using the behavior data to obtain an updated historical portrait.
The video recommendation device provided by the embodiment of the application can be applied to video recommendation equipment. Fig. 3 is a block diagram illustrating a hardware structure of a video recommendation apparatus, and referring to fig. 3, the hardware structure of the video recommendation apparatus may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;
in the embodiment of the application, the number of the processor 1, the communication interface 2, the memory 3 and the communication bus 4 is at least one, and the processor 1, the communication interface 2 and the memory 3 complete mutual communication through the communication bus 4;
the processor 1 may be a central processing unit CPU, or an application Specific Integrated circuit asic, or one or more Integrated circuits configured to implement embodiments of the present invention, etc.;
the memory 3 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory) or the like, such as at least one disk memory;
wherein the memory stores a program and the processor can call the program stored in the memory, the program for:
acquiring real-time behavior data and a scene of a user;
if the historical portrait corresponding to the user exists, updating the historical portrait by using the real-time behavior data and the scene to obtain the current portrait of the user;
acquiring a video recommendation set matched with the current portrait;
pushing the video recommendation set to the user.
Alternatively, the detailed function and the extended function of the program may be as described above.
Embodiments of the present application further provide a storage medium, where a program suitable for execution by a processor may be stored, where the program is configured to:
acquiring real-time behavior data and a scene of a user;
if the historical portrait corresponding to the user exists, updating the historical portrait by using the real-time behavior data and the scene to obtain the current portrait of the user;
acquiring a video recommendation set matched with the current portrait;
pushing the video recommendation set to the user.
Alternatively, the detailed function and the extended function of the program may be as described above.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for video recommendation, comprising:
acquiring real-time behavior data and a scene of a user;
if the historical portrait corresponding to the user exists, updating the historical portrait by using the real-time behavior data and the scene to obtain the current portrait of the user;
acquiring a video recommendation set matched with the current portrait;
pushing the video recommendation set to the user.
2. The method of claim 1, further comprising:
and if the historical portrait corresponding to the user does not exist, pushing a preset video recommendation set to the user.
3. The method of claim 1, wherein obtaining a set of video recommendations that match the current representation comprises:
and acquiring recommended videos with the target quantity matched with the current portrait to form a video recommendation set, wherein the target quantity is a preset video recommendation quantity.
4. The method of claim 1, wherein obtaining a set of video recommendations that match the current representation further comprises:
determining a video recall strategy according to the current portrait;
and acquiring a video recommendation set matched with the video recall strategy.
5. The method of claim 4, wherein the obtaining a set of video recommendations that match the video recall policy comprises:
acquiring a candidate video recommendation set matched with the video recall strategy;
and editing the recommended videos in the candidate video set and sequencing the recommended videos according to preset service interference and a video display environment to obtain a video recommended set, wherein the editing comprises adding and/or deleting.
6. The method of claim 1, further comprising, prior to said pushing the set of video recommendations to the user:
and adding preset display information to the recommended videos in the video recommendation set.
7. The method of claim 1, wherein the historical representation creating process comprises:
acquiring behavior data of the user in a set period;
and updating the historical portrait of the user by using the behavior data to obtain an updated historical portrait.
8. A video recommendation apparatus, comprising:
the data acquisition unit is used for acquiring real-time behavior data of a user and a scene where the user is located;
the portrait updating unit is used for updating the historical portrait by using the real-time behavior data and a scene to obtain the current portrait of the user if the historical portrait corresponding to the user exists;
the video set acquisition unit is used for acquiring a video recommendation set matched with the current portrait;
and the video set pushing unit is used for pushing the video recommendation set to the user.
9. A video recommendation device comprising a memory and a processor;
the memory is used for storing programs;
the processor, executing the program, performs the steps of the video recommendation method of any of claims 1-7.
10. A readable storage medium, having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the video recommendation method according to any one of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114661950A (en) * | 2022-05-26 | 2022-06-24 | 飞狐信息技术（天津）有限公司 | Video recommendation method and device |
| CN115037957A (en) * | 2022-06-07 | 2022-09-09 | 北京视达科技有限公司 | Method, device and system for recommending on-demand content based on live program |
| CN117934086A (en) * | 2024-01-30 | 2024-04-26 | 深圳市亚飞电子商务有限公司 | Intelligent marketing method and system based on user habit |
-
2021
- 2021-11-10 CN CN202111327848.1A patent/CN114036342A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114661950A (en) * | 2022-05-26 | 2022-06-24 | 飞狐信息技术（天津）有限公司 | Video recommendation method and device |
| CN115037957A (en) * | 2022-06-07 | 2022-09-09 | 北京视达科技有限公司 | Method, device and system for recommending on-demand content based on live program |
| CN115037957B (en) * | 2022-06-07 | 2024-01-30 | 北京视达科技有限公司 | Method, device and system for recommending on-demand content based on live program |
| CN117934086A (en) * | 2024-01-30 | 2024-04-26 | 深圳市亚飞电子商务有限公司 | Intelligent marketing method and system based on user habit |
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