CN111556368A - Application method and system of AB test in OTT TV and storage medium - Google Patents
Application method and system of AB test in OTT TV and storage medium Download PDFInfo
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- CN111556368A CN111556368A CN202010250109.6A CN202010250109A CN111556368A CN 111556368 A CN111556368 A CN 111556368A CN 202010250109 A CN202010250109 A CN 202010250109A CN 111556368 A CN111556368 A CN 111556368A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/251—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/252—Processing of multiple end-users' preferences to derive collaborative data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
- H04N21/25866—Management of end-user data
- H04N21/25891—Management of end-user data being end-user preferences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
- H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/4508—Management of client data or end-user data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/4508—Management of client data or end-user data
- H04N21/4532—Management of client data or end-user data involving end-user characteristics, e.g. viewer profile, preferences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
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- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Social Psychology (AREA)
- Computer Networks & Wireless Communication (AREA)
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- Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
Abstract
The invention relates to an application method, a system and a storage medium of an AB test in an OTT TV, wherein the application method of the AB test in the OTT TV comprises the following steps: making at least two recommendation strategies, carrying out AB test on the made recommendation strategies, and collecting test data; carrying out data cleaning on the acquired test data; and setting at least two group conditions according to the number of the recommendation strategies, and recommending contents to the televisions corresponding to the two user groups according to two different recommendation strategies respectively according to the cleaned test data. The application method provided by the invention utilizes the characteristics of the AB test to carry out data acquisition and group condition setting, and then recommends different contents to different user groups according to the acquired data, thereby improving the content delivery accuracy and solving the problem that the recommendation result has larger deviation because the same television content recommendation strategy is adopted by all users in the prior art.
Description
Technical Field
The invention relates to the technical field of application of AB tests, in particular to an application method, an application system and a storage medium of the AB tests in an OTT TV.
Background
OTT TV is an abbreviation of "Over The Top TV", and refers to an open internet-based video service, and a terminal may be a television, a computer, a set-Top box, a PAD, a smart phone, and The like. Meaning that the service is provided on top of the network, emphasizing the independence of the service from the physical network. Video programs transmitted via the internet, such as PPS, UUSEE, etc., are transmitted to a display screen (including a television). From the perspective of consumers, the OTT TV is an internet television, and meets the requirements of consumers, and integrates full-function internet televisions with interactive television functions.
The existing recommendation methods for OTT TV programs and other related content are various, such as a television program recommendation method, including: acquiring a program list to be recommended; acquiring a program feature vector of each television program in the program list to be recommended, wherein the program feature vector of the television program indicates the television program category of the television program; acquiring a first interest feature vector, wherein the dimension of the first interest feature vector is the same as that of the program feature vector, and the first interest feature vector indicates the interested television program category input by a user; respectively calculating cosine similarity of the program feature vector of each television program in the program list to be recommended and the first interest feature vector to obtain a first recommended value of each television program in the program list to be recommended, wherein the first recommended value of the television program is equal to the cosine similarity of the program feature vector of the television program and the first interest feature vector; and recommending the television programs with the first recommendation value larger than or equal to a preset threshold value to the user.
For another example: the utility model provides a smart television advertisement recommendation system of crowd divides period, classifies the crowd and beats the label and construct the date attribute, still includes: the advertisement management platform is used for calling the data of the data cache center to update the advertisement information in the cache after the advertisement information changes; the medium resource operation platform is used for the operation work of operation personnel; a big data cleaning program for cleaning the user behavior data, separating the user viewing behavior data and storing for later use; the time-group-crowd prediction algorithm model group runs at fixed time every day, analyzes and processes the user watching behavior data in each time period of the last day of the user, counts crowd labels in which the movie is classified by acquiring movie information watched by the user, calculates the proportion of people used in the time period as the weight value of the crowd in the day, and fuses the data into the data of the date attribute; the data cache center is used for updating data at regular time every day; and the time-interval crowd advertisement recommendation cloud platform is used for receiving requests and feeding back advertisement data.
It can be seen that, in any of the existing television content recommendation methods, the same method is adopted for all terminals, and different recommendation methods are not adopted due to differences among terminals, so that a great deviation still exists in the recommendation result.
It can be seen that the prior art is still in need of improvement and development.
Disclosure of Invention
Based on this, it is necessary to provide an application method, system and storage medium of an AB test in an OTT TV to solve the problem that the recommendation result has a large deviation because the same TV content recommendation strategy is adopted for all users in the prior art.
The technical scheme of the invention is as follows:
a method for applying an AB test to an OTT TV, comprising:
making at least two recommendation strategies, carrying out AB test on the made recommendation strategies, and collecting test data;
carrying out data cleaning on the acquired test data;
and setting at least two group conditions according to the number of the recommendation strategies, and recommending contents to the televisions corresponding to the two user groups according to two different recommendation strategies respectively according to the cleaned test data.
In a further preferred aspect, the test data includes: viewing information, click information, purchase information and user attributes.
In a further preferred embodiment, the step of performing data cleaning on the acquired test data specifically includes: and carrying out ETL data cleaning on the collected test data, and removing noise data.
In a further preferred embodiment, the step of performing ETL data cleansing on the collected test data and removing noise data further includes: and analyzing the cleaned test data, and marking an equipment label, a behavior label and a user attribute label for the test user.
In a further preferred embodiment, the setting of at least two group conditions according to the number of recommended strategies specifically includes: and setting at least two group conditions according to the number of the recommended strategies, and forming a crowd label.
In a further preferred embodiment, the step of setting at least two group conditions according to the number of recommended strategies and forming the crowd label specifically includes: and counting the number of the equipment tags, the behavior tags and the user attribute tags in the test data, setting at least two group conditions according to the number of the recommended strategies, and forming group tags.
In a further preferred embodiment, the step of setting at least two group conditions according to the number of recommended strategies and forming the crowd label further includes: and according to the formed crowd label, the user group is defined.
In a further preferred embodiment, the step of defining the user group according to the formed crowd label further comprises: and respectively matching and binding the plurality of defined user groups and the plurality of established recommendation strategies.
An application system of the AB test on the OTT TV comprises a memory and a processor, wherein the memory stores a computer program, and the computer program realizes the steps of the application method of the AB test on the OTT TV as described above when being executed by the processor.
A storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the AB test application method in OTT TV as described above.
Compared with the prior art, the application method of the AB test in the OTT TV provided by the invention comprises the following steps: making at least two recommendation strategies, carrying out AB test on the made recommendation strategies, and collecting test data; carrying out data cleaning on the acquired test data; and setting at least two group conditions according to the number of the recommendation strategies, and recommending contents to the televisions corresponding to the two user groups according to two different recommendation strategies respectively according to the cleaned test data. The application method provided by the invention utilizes the characteristics of the AB test to carry out data acquisition and group condition setting, and then recommends different contents to different user groups according to the acquired data, thereby improving the content delivery accuracy and solving the problem that the recommendation result has larger deviation because the same television content recommendation strategy is adopted by all users in the prior art.
Drawings
FIG. 1 is a flow chart of the preferred embodiment of the method for applying the AB test to OTT TV in the present invention.
FIG. 2 is a schematic block diagram of a preferred embodiment of the application system of the AB test on OTT TV in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides an application method of an AB test in an OTT TV, which comprises the following steps:
s100, at least two recommendation strategies are formulated, AB testing of the formulated recommendation strategies is carried out, and test data are collected.
The AB test is to make two (A/B) or a plurality of (A/B/n) versions for a Web or App interface or process, make visitor groups with the same (similar) composition randomly access the versions in the same time dimension, collect user experience data and service data of each group, and finally analyze and evaluate the best version to be formally adopted.
In the invention, the AB test is utilized to collect the user's preference degrees to a plurality of pre-made recommendation strategies (data such as whether the number of clicks on a certain recommendation interface is more than that of clicks on other recommendation interfaces of the user and the like are collected from data), so that the television content delivery is more accurately carried out.
Preferably, a plurality of pre-established recommendation strategies can be respectively manufactured according to a plurality of data collection systems, for example, recommendation strategy 1 recommends television programs according to the television program recommendation method described in the background art, and recommends advertisements according to the intelligent television advertisement recommendation system for time-share crowd division described in the background art; the recommendation policy 2 recommends tv programs and advertisements in another way (it is also possible to use new ones, which is not specifically limited by the present invention), and the contents of the recommended programs, advertisements, and applications between the two recommendation policies may be partially the same or all different.
For example, there are two established recommendation strategies, and recommendation strategy 1 includes: the television program recommendation method a + the advertisement recommendation method X, the recommendation policy 2 may include: the television program recommendation method A + the advertisement recommendation method Y, or the recommendation strategy 2, comprises: the television program recommendation method B + the advertisement recommendation method X, or the recommendation policy 2 may also include: television program recommendation method B + advertisement recommendation method Y.
For another example, there are three established recommendation strategies, and recommendation strategy 1 includes: a television program recommendation method a + an advertisement recommendation method X; then recommendation policy 2 may include: television program recommendation method a + advertisement recommendation method Y, then recommendation policy 3 includes: television program recommendation method B + advertisement recommendation method Y (or television program recommendation method B + advertisement recommendation method X).
For another example, there are three established recommendation strategies, and recommendation strategy 1 includes: a television program recommendation method A + an advertisement recommendation method B + an application recommendation method C; then recommendation policy 2 may include: the television program recommendation method a + the advertisement recommendation method B + the application recommendation method Z, and then the recommendation policy 3 includes: a television program recommendation method X + an advertisement recommendation method B + an application recommendation method C (or a television program recommendation method X + an advertisement recommendation method Y + an application recommendation method C, or a television program recommendation method X + an advertisement recommendation method Y + an application recommendation method Z, or a television program recommendation method a + an advertisement recommendation method Y + an application recommendation method Z, etc.); the present invention will not be described in detail in other cases which can be easily inferred, but the inferred contents are all within the protection scope of the present invention.
Preferably, the test data includes: viewing information, click information, purchase information and user attributes. It is easy to understand that viewing information, i.e. a program playing record (continuous or discontinuous) of a certain television or a certain account (the channel of the information source is different according to the selection of the test object) in a period of time (may be counted in a period of time in the prior art, and is not described any further). The click information includes: program click information (interested in a program) and application click information, etc.
And S200, carrying out data cleaning on the acquired test data. Specifically, ETL data cleaning is carried out on the collected test data, and noise data are removed.
ETL, an abbreviation used in english Extract-Transform-Load, is used to describe the process of extracting (Extract), converting (Transform), and loading (Load) data from a source end to a destination end. The ETL is a process of loading data of a business system into a data warehouse after extraction, cleaning and conversion, aims to integrate scattered, disordered and standard non-uniform data in an enterprise and provides an analysis basis for the decision of the enterprise, and is an important link of a BI (business intelligence) project.
The step of cleaning the ETL data of the collected test data and removing the noise data further comprises the following steps: and analyzing the cleaned test data, and marking an equipment label, a behavior label and a user attribute label for the test user.
S300, setting at least two group conditions according to the number of the recommendation strategies, and recommending contents to the televisions corresponding to the two user groups according to two different recommendation strategies respectively according to the cleaned test data.
Further, the setting of at least two group conditions according to the number of recommended strategies specifically includes: and setting at least two group conditions according to the number of the recommended strategies, and forming a crowd label.
Preferably, the step of setting at least two group conditions according to the number of recommended strategies and forming a group label further includes: and according to the formed crowd label, the user group is defined.
Preferably, the step of defining the user group according to the formed crowd label further comprises: and respectively matching and binding the plurality of defined user groups and the plurality of established recommendation strategies.
As shown in fig. 2, the present invention further provides an application system of the AB test on the OTT TV, which includes a memory 10 and a processor 20, wherein the memory 10 stores a computer program, and the computer program is executed by the processor 20 to implement the steps of the method for applying the AB test on the OTT TV.
The present invention also provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of applying AB tests on OTT TV as described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (SyNchlinNk) DRAM (SLDRAM), Rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An application method of an AB test in an OTT TV is characterized by comprising the following steps:
making at least two recommendation strategies, carrying out AB test on the made recommendation strategies, and collecting test data;
carrying out data cleaning on the acquired test data;
and setting at least two group conditions according to the number of the recommendation strategies, and recommending contents to the televisions corresponding to the two user groups according to two different recommendation strategies respectively according to the cleaned test data.
2. The method for applying an AB test to OTT TV according to claim 1, wherein the test data comprises: viewing information, click information, purchase information and user attributes.
3. The method for applying the AB test to the OTT TV according to claim 1, wherein the step of performing data cleansing on the collected test data specifically comprises: and carrying out ETL data cleaning on the collected test data, and removing noise data.
4. The method for applying AB test to OTT TV according to claim 1, wherein the step of performing ETL data cleansing on the collected test data and removing noise data further comprises: and analyzing the cleaned test data, and marking an equipment label, a behavior label and a user attribute label for the test user.
5. The method for applying the AB test to the OTT TV according to claim 4, wherein the setting of at least two population conditions according to the number of recommended strategies is specifically: and setting at least two group conditions according to the number of the recommended strategies, and forming a crowd label.
6. The method for applying the AB test to the OTT TV according to claim 5, wherein the steps of setting at least two population conditions according to the number of recommended strategies and forming the population label are specifically as follows: and counting the number of the equipment tags, the behavior tags and the user attribute tags in the test data, setting at least two group conditions according to the number of the recommended strategies, and forming group tags.
7. The method for applying an AB test to OTT TV according to claim 5, wherein said step of setting at least two population conditions according to the number of recommended strategies and forming a population label further comprises, after said step of: and according to the formed crowd label, the user group is defined.
8. The method for using AB test on OTT TV as claimed in claim 7, wherein said step of delineating a user population according to the formed population label further comprises: and respectively matching and binding the plurality of defined user groups and the plurality of established recommendation strategies.
9. An application system of AB test on OTT TV, comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, implements the steps of the method of AB test application on OTT TV as claimed in any one of claims 1 to 8.
10. A 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 AB test application method of any one of claims 1 to 8 on OTT TV.
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