阅读说明：本技术 节目推荐方法、装置、电子设备及可读存储介质 (Program recommendation method and device, electronic equipment and readable storage medium ) 是由 李鸣 金泽 沈伟 付强 张良益 于 2020-06-15 设计创作，主要内容包括：本申请提供一种节目推荐方法、装置、电子设备及可读存储介质,方法通过获取预设特征表中各节目标签的价值分布(预设特征表对应的价值分布,是根据历史实际收视过的节目所对应的节目标签确定出的对各节目标签偏好的分布),根据价值分布从确定出推荐标签集,根据各节目与推荐标签集的匹配度确定出待推荐节目集合进行推荐。这样,通过对各节目设置标签,并通过历史实际收视过的节目来实现对于节目标签价值分布的确定,从而使得各节目标签得以与用户的实际收视行为强相关,分析出用户所偏好的标签。进而可以根据节目标签的价值分布实现对于待推荐节目集合的确定,能够使得推荐的节目能更贴合用户实际需要,可以实现对于大屏终端用户的可靠性推荐。(The method comprises the steps of obtaining value distribution of program labels in a preset feature list (the value distribution corresponding to the preset feature list is the distribution of preference of the program labels determined according to the program labels corresponding to the programs which are actually watched historically), determining a recommended label set according to the value distribution, and determining a set of programs to be recommended according to the matching degree of the programs and the recommended label set for recommendation. Therefore, the program label value distribution is determined by setting labels for the programs and through the history of the programs which are actually watched, so that the program labels are strongly related to the actual watching behaviors of the user, and the labels preferred by the user are analyzed. And then, the determination of the set of the programs to be recommended can be realized according to the value distribution of the program labels, the recommended programs can better meet the actual needs of users, and the reliable recommendation of large-screen terminal users can be realized.)

1. A program recommendation method, comprising:

obtaining the value distribution of each program label in a preset feature list; the value distribution corresponding to the preset feature table is determined according to program labels corresponding to the programs which are actually watched historically, and the value distribution is preferential to the program labels;

determining a recommended label set from the program labels corresponding to the preset feature table according to the value distribution;

determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set;

and recommending each program to be recommended in the program set to be recommended.

2. The program recommendation method of claim 1, wherein the preset feature table is a feature table corresponding to a target time period at a current time; the feature table corresponding to the target time interval is determined according to the program labels corresponding to the programs which are actually watched historically in the target time interval, and the preference distribution of the program labels is given to each program.

3. The program recommendation method of claim 1, wherein determining a recommendation tag set from program tags corresponding to the preset feature table according to the value distribution comprises:

and determining a recommended label set from the program labels corresponding to the preset feature table by adopting a dobby algorithm according to the value distribution.

4. The program recommendation method of claim 3 wherein determining a set of recommended tags from the program tags corresponding to the predetermined profile using a dobby algorithm based on the value profile comprises:

acquiring the viewing completion degree p of the last viewed program_n-1；

According to said p_n-1Determining a comparison value;

determining a random number according to the value distribution;

when the random number is larger than the comparison value, extracting a program label with the maximum value from all program labels of the feature list as a recommendation label; when the random number is less than or equal to the comparison value, randomly extracting a program label from all program labels of the feature table as a recommendation label;

determining a random number again according to the value distribution; when the random number is larger than the comparison value, extracting a program label with the maximum value from the rest program labels of the feature list as a recommendation label; when the random number is less than or equal to the comparison value, randomly extracting a program label from the rest program labels of the feature table as a recommendation label; and until the number of the extracted recommended labels reaches a preset number.

5. The program recommendation method of claim 3, wherein the preset program set comprises a preset on-demand program set;

determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set, wherein the method comprises the following steps:

and determining a set of on-demand programs to be recommended according to the matching degree of each on-demand program in the on-demand program set and the recommendation label set.

6. The program recommendation method of claim 1, wherein determining a recommendation tag set from program tags corresponding to the preset feature table according to the value distribution comprises:

inputting the value distribution into a trained time sequence prediction model to obtain predicted label value distribution;

and determining n program labels with the highest value as recommended labels according to the predicted label value distribution.

7. The program recommendation method of claim 6, wherein said timing prediction model is a Transformer model.

8. The program recommendation method of claim 6, wherein the preset program set comprises a preset live program set;

determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set, wherein the method comprises the following steps:

and determining a set of live programs to be recommended according to the matching degree of each live program in the live program set and the recommended label set.

9. The program recommendation method of claim 1, wherein before recommending each program to be recommended in the set of programs to be recommended, the method further comprises:

and adding a preset program to be recommended into the program set to be recommended.

10. The program recommendation method of claim 1, wherein said recommending said program to be recommended comprises:

arranging the programs to be recommended according to a preset rule;

and recommending the arranged programs to be recommended.

11. The program recommendation method of claim 10, further comprising: obtaining a value tensor T corresponding to the value distribution; the value tensor T is formed by sequentially arranging the values of the program labels in the feature table as tensor elements;

arranging the programs to be recommended according to a preset rule pair, comprising:

determining a value tensor T corresponding to each program to be recommended_i(ii) a The T is_iIs consistent with the program label arrangement of the TAnd said T is_iThe value of each program label is that each program label is in the T_iThe proportion of the corresponding program to be recommended;

calculating each of the T_iA distance from the T;

arranging the T according to the sequence of the distance from small to large_iThe corresponding program to be recommended is selected,

and recommending the arranged programs to be recommended.

12. The program recommendation method of claim 2, further comprising:

after the viewing of any program is finished, acquiring the starting viewing time, the viewing completion degree and the program label of the program;

determining the behavior value of the program according to the audience rating completion degree of the program;

determining the value of each program label according to the behavior value and the preset proportion of each program label of the program in the program;

and updating the characteristic table of the time period of the initial viewing time according to the value of each program label.

13. The program recommendation method of claim 12, wherein said method further comprises: acquiring the viewing completion degree of the last viewed program;

determining the behavior value of the program according to the audience rating of the program, comprising:

and determining the behavior value of the program according to the viewing completion degree of the program and the behavior value corresponding to the last viewed program.

14. The method of claim 12, wherein determining the value of each program label based on the behavioral value and a predetermined percentage of each program label in the program comprises:

determining a value increment corresponding to each program label according to the behavior value and the preset proportion of each program label in the program;

acquiring the original value corresponding to each program label in a feature list of the time period of the initial viewing time;

and determining the latest value of each program label according to the original value and the value increment corresponding to each program label.

15. The program recommendation method of any one of claims 1-14, further comprising:

acquiring corresponding behavior values of preset program types; the behavior value corresponding to each program type is a value which is determined according to the program of the type actually watched by the history and is used for representing the preference condition of the user to the program type;

determining a user preference type according to the behavior value corresponding to each program type;

determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set, wherein the method comprises the following steps:

and determining a set of programs to be recommended which meet the requirement of the user preference type from a preset program set according to the matching degree of each program in the preset program set and the recommended label set.

16. The program recommendation method of claim 15, wherein determining the user preference type according to the behavior value corresponding to each program type comprises:

acquiring the mean value of the behavior values corresponding to the program types;

and determining the program type with the behavior value being more than or equal to the average value as a user preference type.

17. A program recommendation device, comprising: the system comprises an acquisition module, a processing module and a recommendation module;

the acquisition module is used for acquiring the value distribution of each program label in the preset feature list; the value distribution corresponding to the preset feature table is determined according to program labels corresponding to the programs which are actually watched historically, and the value distribution is preferential to the program labels;

the processing module is used for determining a recommended label set from the program labels corresponding to the preset feature table according to the value distribution; determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set;

and the recommending module is used for recommending each program to be recommended in the program set to be recommended.

18. An electronic device comprising a processor, a memory, and a communication bus;

the communication bus is used for realizing connection communication between the processor and the memory;

the processor is configured to execute one or more programs stored in the memory to implement the program recommendation method of any one of claims 1 to 16.

19. A readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the program recommendation method of any one of claims 1 to 16.

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a program recommendation method and apparatus, an electronic device, and a readable storage medium.

Background

With the continuous development of terminal intelligent technology, more and more intelligent terminal products enter the daily life of people. Large-screen terminals such as televisions have become indispensable electronic devices for homes.

However, large-screen terminals such as televisions are content providers, and many large-screen terminals have a program recommendation function in order to enable users to have better content viewing experience. However, most of the current program recommendation functions are the latest and hottest programs in the recommended content market, which results in that the recommended programs are not fit to the actual needs of the user.

Disclosure of Invention

The embodiment of the application aims to provide a program recommendation method, a program recommendation device, electronic equipment and a readable storage medium, which are used for realizing reliable recommendation of a large-screen terminal user.

The embodiment of the application provides a program recommendation method, which comprises the following steps: obtaining the value distribution of each program label in a preset feature list; the value distribution corresponding to the preset feature table is determined according to program labels corresponding to the programs which are actually watched historically, and the value distribution is preferential to the program labels; determining a recommended label set from the program labels corresponding to the preset feature table according to the value distribution; determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set; and recommending each program to be recommended in the program set to be recommended.

In the implementation process, the value distribution of the program labels is determined by setting the labels for the programs and by history of the programs actually watched, so that the program labels are strongly related to the actual watching behaviors of the user, and the labels preferred by the user are obtained through analysis. And then, the determination of the set of the programs to be recommended can be realized according to the value distribution of the program tags, so that the recommended programs to be recommended can better accord with the actual viewing preference of the user, the actual needs of the user can be better met, and the reliable recommendation of the large-screen terminal user can be realized.

Further, the preset feature table is a feature table corresponding to a target time period in which the current time is located; the feature table corresponding to the target time interval is determined according to the program labels corresponding to the programs which are actually watched historically in the target time interval, and the preference distribution of the program labels is given to each program.

For a large-screen terminal such as a television, the terminal is usually used by a plurality of users of a specific group as a common device (for example, for a television, the terminal is usually used by all members in a family). In general, different users tend to have relatively significant differences in usage time for the use of large screen terminals. For example, for a television, if a child likes to watch animation and a parent likes to watch a television of the golden file, the child is using the television usually around 5 to 7 o 'clock, and 7 o' clock is followed by the parent. Therefore, by dividing each time interval in advance, setting the characteristic table corresponding to each time interval and determining the program to be recommended in the current time interval based on the characteristic table in the corresponding time interval, the recommended program to be recommended can better accord with the actual viewing preferences of different users to a certain extent, the recommended program to be recommended better conforms to the actual living needs, and the reliable recommendation of a large-screen terminal user can be realized.

Further, determining a recommended label set from the program labels corresponding to the preset feature table according to the value distribution, including: and determining a recommended label set from the program labels corresponding to the preset feature table by adopting a dobby algorithm according to the value distribution.

It should be appreciated that the dobby algorithm is a good reinforcement learning algorithm. Through the multi-betting algorithm, a relatively optimal recommendation label set can be selected from program labels in the embodiment of the application, so that the recommendation accuracy of the scheme of the application is improved.

Further, according to the value distribution, determining a recommended label set from the program labels corresponding to the preset feature table by using a dobby algorithm, including: acquiring the viewing completion degree p of the last viewed program_n-1(ii) a According to said p_n-1Determining a comparison value; determining a random number according to the value distribution; when the random number is larger than the comparison value, extracting a program label with the maximum value from all program labels of the feature list as a recommendation label; when the random number is less than or equal to the comparison value, randomly extracting a program label from all program labels of the feature table as a recommendation label; determining a random number again according to the value distribution; when the random number is larger than the comparison value, extracting a program label with the maximum value from the rest program labels of the feature list as a recommendation label; when the random number is less than or equal to the comparison value, randomly extracting a program label from the rest program labels of the feature table as a recommendation label; and until the number of the extracted recommended labels reaches a preset number.

In the above implementation process, the viewing completion degree p of the last viewed program is used_n-1The comparison value is determined, so that the watching completion degree of the program watched in the past does not need to be stored in the equipment, and the data storage pressure can be reduced. Simultaneously adopting the viewing completion degree p of the last viewed program_n-1The comparison value is determined, and the comparison value can be more suitable for the actual use behavior of the user, so that the extracted recommendation label can more probably meet the actual needs of the user.

Further, the preset program set comprises a preset on-demand program set; determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set, wherein the method comprises the following steps: and determining a set of on-demand programs to be recommended according to the matching degree of each on-demand program in the on-demand program set and the recommendation label set.

Further, determining a recommended label set from the program labels corresponding to the preset feature table according to the value distribution, including: inputting the value distribution into a trained time sequence prediction model to obtain predicted label value distribution; and determining n program labels with the highest value as recommended labels according to the predicted label value distribution.

Further, the time sequence prediction model is a Transformer model.

Further, the preset program set comprises a preset live program set; determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set, wherein the method comprises the following steps: and determining a set of live programs to be recommended according to the matching degree of each live program in the live program set and the recommended label set.

In the practical application process, the live programs have a relatively fixed playing start time, so that each live program has a fixed viewable time period, and a user must watch the programs in the time period. Therefore, for live programs, the live programs are predicted through the time sequence model, the live programs can be predicted by combining the viewing time sequence of the programs viewed in history, and the programs to be recommended which are more suitable for the actual needs of the user are obtained.

Further, before recommending each program to be recommended in the set of programs to be recommended, the method further includes: and adding a preset program to be recommended into the program set to be recommended.

Further, the recommending the program to be recommended includes: arranging the programs to be recommended according to a preset rule; and recommending the arranged programs to be recommended.

Further, the method further comprises: obtaining a value tensor T corresponding to the value distribution; the value tensor T is formed by sequentially arranging the values of the program labels in the feature table as tensor elements; arranging the programs to be recommended according to a preset rule pair, comprising: determining a value tensor T corresponding to each program to be recommended_i(ii) a The T is_iAnd the placeThe program labels of the T are arranged consistently, and the T is_iThe value of each program label is that each program label is in the T_iThe proportion of the corresponding program to be recommended; calculating each of the T_iA distance from the T; arranging the T according to the sequence of the distance from small to large_iAnd recommending the arranged programs to be recommended according to the corresponding programs to be recommended.

In the implementation process, the value tensor T corresponding to each program to be recommended is used_iThe distance from the T can determine the coincidence degree of each program to be recommended and the feature table, namely, the matching degree of each program to be recommended and the user preference can be estimated. T is_iThe smaller the distance from T, the better the matching of the program to be recommended and the user preference can be estimated, so the T with the smaller distance can be used_iThe corresponding programs to be recommended are arranged in front so as to improve the recommendation effect.

Further, the method further comprises: after the viewing of any program is finished, acquiring the starting viewing time, the viewing completion degree and the program label of the program; determining the behavior value of the program according to the audience rating completion degree of the program; determining the value of each program label according to the behavior value and the preset proportion of each program label of the program in the program; and updating the characteristic table of the time period of the initial viewing time according to the value of each program label.

In the implementation process, after the viewing of any program is finished, the feature table of the time slot in which the program is located can be updated according to the viewed program, so that the expression accuracy of the feature table is continuously improved.

Further, the method further comprises: acquiring the viewing completion degree of the last viewed program; determining the behavior value of the program according to the audience rating of the program, comprising: and determining the behavior value of the program according to the viewing completion degree of the program and the behavior value corresponding to the last viewed program.

Further, determining the value of each program label according to the behavior value and the preset proportion of each program label in the program, including: determining a value increment corresponding to each program label according to the behavior value and the preset proportion of each program label in the program; acquiring the original value corresponding to each program label in a feature list of the time period of the initial viewing time; and determining the latest value of each program label according to the original value and the value increment corresponding to each program label.

Further, the method further comprises: acquiring corresponding behavior values of preset program types; the behavior value corresponding to each program type is a value which is determined according to the program of the type actually watched by the history and is used for representing the preference condition of the user to the program type; determining a user preference type according to the behavior value corresponding to each program type; determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set, wherein the method comprises the following steps: and determining a set of programs to be recommended which meet the requirement of the user preference type from a preset program set according to the matching degree of each program in the preset program set and the recommended label set.

In practical applications, users often have preferences for the type of program. In the implementation process, the preference of the user for the program types can be effectively analyzed and obtained by obtaining the behavior value of each program type, so that the program can be recommended only aiming at the program with the user preference type when the program is recommended, and further the recommended program to be recommended can accord with the actual viewing preference of the user and better meet the actual needs of the user.

Further, determining the user preference type according to the behavior value corresponding to each program type comprises the following steps: acquiring the mean value of the behavior values corresponding to the program types; and determining the program type with the behavior value being more than or equal to the average value as a user preference type. An embodiment of the present application further provides a program recommending apparatus, including: the method comprises the following steps: the system comprises an acquisition module, a processing module and a recommendation module; the acquisition module is used for acquiring the value distribution of each program label in the preset feature list; the value distribution corresponding to the preset feature table is determined according to program labels corresponding to the programs which are actually watched historically, and the value distribution is preferential to the program labels; the processing module is used for determining a recommended label set from the program labels corresponding to the preset feature table according to the value distribution; determining a set of programs to be recommended according to the matching degree of each program in a preset program set and the recommended label set; and the recommending module is used for recommending each program to be recommended in the program set to be recommended.

An embodiment of the present application further provides an electronic device, including: a processor, a memory, and a communication bus; the communication bus is used for realizing connection communication between the processor and the memory; the processor is configured to execute one or more programs stored in the memory to implement the program recommendation method as any one of the above.

An embodiment of the present application further provides a readable storage medium, where one or more programs are stored, and the one or more programs are executable by one or more processors to implement any of the above program recommendation methods.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

Fig. 1 is a schematic flowchart of a program recommendation method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a recommendation list provided in an embodiment of the present application;

FIG. 3 is a schematic diagram of another recommendation list provided by an embodiment of the present application;

FIG. 4 is a diagram illustrating an embodiment of an intensity distribution tensor T provided by the present application;

fig. 5 is a schematic structural diagram of a program recommending apparatus according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.