CN106792172A - A kind of method of internet television personalized recommendation video - Google Patents
A kind of method of internet television personalized recommendation video Download PDFInfo
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- CN106792172A CN106792172A CN201611156617.8A CN201611156617A CN106792172A CN 106792172 A CN106792172 A CN 106792172A CN 201611156617 A CN201611156617 A CN 201611156617A CN 106792172 A CN106792172 A CN 106792172A
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- 238000000034 method Methods 0.000 title abstract description 28
- 230000006399 behavior Effects 0.000 description 10
- 238000012216 screening Methods 0.000 description 7
- 238000011156 evaluation Methods 0.000 description 4
- 230000002349 favourable effect Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
Classifications
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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
-
- 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
-
- 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
- H04N21/458—Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming stream; Updating operations, e.g. for OS modules ; time-related management operations
- H04N21/4586—Content update operation triggered locally, e.g. by comparing the version of software modules in a DVB carousel to the version stored locally
-
- 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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- 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)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
A kind of method of internet television personalized recommendation video, comprises the following steps：1) user behavior is collected, label data is obtained；2) label hot value in the tag database of each label of storage is updated, the tag database is made up of two parts：Data buffer zone and file storage area；3) according to data genaration hot topic label in tag database；4) video is recommended according to each popular label generation.A kind of method of internet television personalized recommendation video of the invention, is locally completed by terminal, by analyzing the operation behavior of user, recommends the program that he may like, so as to realize personalized recommendation.
Description
Technical Field
The invention relates to an internet television video playing method. In particular to a method for recommending videos individually by an internet television.
Background
The internet television is a television all-in-one machine or a television set top box with an internet content transmission channel, and users can enjoy internet video content through a television screen. Most video recommendation modes are non-personalized, and non-personalized recommendations mainly use a single dimension plus a half-life to see a global ranking, such as click ranking within 30 days and weekly popular ranking. The non-personalized recommendation has low efficiency and lacks pertinence, is probably not interesting for users, and can cause a Martian effect, more people are listed, more people pass through the recommendation points, stronger people are stronger, weaker people are less and weaker, the bipolar differentiation is possibly serious, some high-quality materials are buried, in order to solve the Martian effect problem, mainly in compliance with a data and automatic mode, personalized recommendation needs to be added, the personalized advantage is good in experience, the efficiency is greatly improved, and the users can find interesting things more quickly.
In the video recommendation mode in the prior art, as shown in fig. 1, a play record of a user is uploaded to a data center of a server, a popular movie ranking list is generated after data analysis, and terminal equipment acquires ranking list data from the server and displays the ranking list data to the user as movie recommendation. The prior art has the following disadvantages
1. Background support is needed, the development period is long, and the cost is high;
2. the global ranking is seen with a single dimension and a half-life period, for example, the click ranking and the hot one week ranking within 30 days, the non-personalized recommendation is low in efficiency and not targeted, the users are likely not to be really interested in, and a Martian effect is caused.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for the personalized recommendation of videos of the internet television, which is completed locally by a terminal.
The technical scheme adopted by the invention is as follows: a method for recommending videos by Internet television in a personalized manner comprises the following steps:
1) collecting user behaviors and acquiring label data;
2) updating the label heat value in a label database for storing each label, wherein the label database comprises two parts: a data buffer and a file storage area;
3) generating a hot label according to data in the label database;
4) and generating a recommended video according to each hot label.
The user behavior in step 1) refers to a specific operation performed by a user on a video program, and after the user operates a certain program, the tag attribute owned by the program is acquired, and the collected user behavior includes: entering a program detail page, playing a video and finishing watching the video.
The programs are labeled according to different attributes, and the labels of the programs are summarized into four types of label data including types, sub-types, a main actor and a director; the types are divided into movies, TV shows, cartoons, comprehensive arts, sports and science education, and each type has a respective subtype below.
The hot value in the step 2) refers to the operation times of the user on the labels of the same type.
The step 2) comprises the following steps:
(1) the operation behavior of a user on any type of label triggers a label hot value adding interface of a label database, and the label hot value adding interface increases the hot value of a corresponding label in a data buffer area;
(2) the label data of the data buffer area is updated to the file storage area at regular time, so that the data in the data buffer area is prevented from being lost; the method specifically comprises the following steps:
the label data in the data buffer area is stored in the file storage area according to a certain storage format, and the four types of label data file storage formats are defined as follows:
type tag storage format: 0, type, heat value;
subtype tag storage format: 1, subtype, heat value, type to which subtype belongs;
the master tag storage format: 2, lead actor, heat value;
director label storage format: 3, director, heat value;
the tag data storage formats are separated by a symbol | therebetween.
The step 3) comprises the following steps: respectively generating a hot type label, a hot director label and a hot director label on the basis of the heat value of each label in the label database; wherein,
the generation process of the hot type label comprises the following steps: the method comprises the following steps of (1) taking type labels of the first two ranked heat values, and selecting one of the labels which is used relatively less as a hot type label according to the use records of the labels of the first two ranked heat values;
the generation process of the hot type label comprises the following steps: taking the sub-type label with the highest heat value under the hot type label as the hot sub-type label;
the generation process of the hot lead actor label: and taking the leading actor labels of the two first ranked heat values, and selecting and using a relatively small number of the leading actor labels as the hot actor labels according to the use records of the leading actor labels of the two first ranked heat values.
The generation process of the hot director label comprises the following steps: and taking the director labels of the first two ranked according to the heat value, and selecting and using one of the director labels as a hot director label according to the use records of the director labels of the first two ranked.
Step 4) comprises the steps of screening out three popular programs:
(1) acquiring a program set list from a server side according to the popular type label and the popular subtype label, comparing historical records, removing the watched programs, and screening out eight programs according to the goodness of each program;
(2) acquiring a program set list from a server according to a popular lead actor tag, comparing historical records, removing watched programs, and screening out two programs according to the favorable evaluation degree of each program;
(3) acquiring a program set list from a server according to a hot director label, comparing historical records, removing watched programs, and screening out two programs according to the favorable evaluation degree of each program;
the three popular programs are combined to form the video program recommended to the user at one time.
The method for recommending videos individually by the internet television is finished locally by the terminal, and programs which the user may like are recommended by analyzing the operation behaviors of the user, so that the individual recommendation is realized. Has the following advantages
1. Background support of a recommendation system is not needed, development period is short, and cost is low;
2. personalized recommendation based on the user browsing records has strong pertinence, a user can quickly find an interested film, and the user experience is obviously improved.
Drawings
FIG. 1 is a flow chart of a prior art video recommendation method;
fig. 2 is a flowchart of a method for recommending videos by internet television personalization according to the present invention.
Detailed Description
The following describes in detail a method for recommending videos individually by using an internet tv according to an embodiment of the present invention with reference to the accompanying drawings.
As shown in fig. 2, the method for recommending videos by internet television in a personalized manner of the present invention includes the following steps:
1) collecting user behaviors and acquiring label data;
the user behavior refers to specific operation of a user on a video program, when the user operates a certain program, the tag attribute owned by the program is acquired, and the collected user behavior comprises the following steps: entering a program detail page, playing a video and finishing watching the video. The programs are labeled according to different attributes, and the labels of the programs are summarized into four types of label data including types, sub-types, a director and a director; the types of the videos are divided into movies, TV shows, animations, comprehensive arts, sports and science education, and each type has a respective subtype below, for example, the subtypes of the movies are drama, love, action, comedy, horror, science fiction, suspicion, animation, war, magic, swordsman, record, ethics and micro-movies.
2) Updating and storing label heat value in a label database of each label, wherein the heat value refers to the operation number value of a user on the same type of label, and the label database comprises two parts: a data buffer and a file storage area; the method comprises the following steps:
(1) the operation behavior of a user on any type of label triggers a label hot value adding interface of a label database, and the label hot value adding interface increases the hot value of a corresponding label in a data buffer area;
(2) the label data of the data buffer area is updated to the file storage area at regular time, so that the data in the data buffer area is prevented from being lost; the method specifically comprises the following steps:
the label data in the data buffer area is stored in the file storage area according to a certain storage format, and the four types of label data file storage formats are defined as follows:
type tag storage format: 0, type, heat value;
subtype tag storage format: 1, subtype, heat value, type to which subtype belongs;
the master tag storage format: 2, lead actor, heat value;
director label storage format: 3, director, heat value;
the label data storage formats are separated by a symbol | in the form of: 0, movie, 13|0, drama, 10|1, action, 13, movie |1, suspense, 2, movie |2, tom, 4|3, mike, 5.
3) Generating a hot label according to data in the label database; the method comprises the following steps:
respectively generating a hot type label, a hot director label and a hot director label on the basis of the heat value of each label in the label database; wherein,
the generation process of the hot type label comprises the following steps: the method comprises the following steps of (1) taking the type labels of the first two ranked heat values, and selecting and using a relatively fewer one as a hot type label according to the use records of the labels of the first two ranked heat values to prevent the hot type labels from always being the same;
the generation process of the hot type label comprises the following steps: taking the sub-type label with the highest heat value under the hot type label as the hot sub-type label;
the generation process of the hot lead actor label: and taking the leading labels of the two first ranked heat values, and selecting and using a relatively small number of leading labels as hot leading labels according to the use records of the leading labels of the two first ranked heat values to prevent the hot type labels from being always the same.
The generation process of the hot director label comprises the following steps: and taking the director labels with the first two ranked heat values, and selecting and using a relatively small number of the director labels as the hot director labels according to the use records of the director labels with the first two ranked heat values to prevent the hot type labels from being always the same.
4) And generating a recommended video according to each popular label, wherein the recommended video comprises three popular programs which are screened out:
(1) acquiring a program set list from a server side according to the popular type label and the popular subtype label, comparing historical records, removing the watched programs, and screening out eight programs according to the goodness of each program;
(2) acquiring a program set list from a server according to a popular lead actor tag, comparing historical records, removing watched programs, and screening out two programs according to the favorable evaluation degree of each program;
(3) acquiring a program set list from a server according to a hot director label, comparing historical records, removing watched programs, and screening out two programs according to the favorable evaluation degree of each program;
the three popular programs are combined to form the video program recommended to the user at one time.
Therefore, the method for recommending videos individually by the internet television does not need big data support of a server, is completed locally by the terminal, and recommends programs which the user may like by analyzing the operation behaviors of the user, thereby realizing individual recommendation.
Claims (7)
1. A method for recommending videos by Internet television in a personalized way is characterized by comprising the following steps:
1) collecting user behaviors and acquiring label data;
2) updating the label heat value in a label database for storing each label, wherein the label database comprises two parts: a data buffer and a file storage area;
3) generating a hot label according to data in the label database;
4) and generating a recommended video according to each hot label.
2. The method as claimed in claim 1, wherein the user behavior in step 1) refers to a specific operation performed by the user on a video program, and when the user operates a certain program, the tag attribute owned by the program is obtained, and the collected user behavior includes: entering a program detail page, playing a video and finishing watching the video.
3. The method for personalized recommendation of videos for internet televisions according to claim 2, wherein the programs are tagged according to different attributes, and tags of the programs are summarized into four types of tag data including genre, subtype, director and director; the types are divided into movies, TV shows, cartoons, comprehensive arts, sports and science education, and each type has a respective subtype below.
4. The method for personalized recommendation of videos by internet television as claimed in claim 1, wherein the heat value in step 2) is a numerical value of operation times of the user on the same type of tag.
5. The method for recommending videos by internet television in a personalized manner according to claim 1, wherein the step 2) comprises:
(1) the operation behavior of a user on any type of label triggers a label hot value adding interface of a label database, and the label hot value adding interface increases the hot value of a corresponding label in a data buffer area;
(2) the label data of the data buffer area is updated to the file storage area at regular time, so that the data in the data buffer area is prevented from being lost; the method specifically comprises the following steps:
the label data in the data buffer area is stored in the file storage area according to a certain storage format, and the four types of label data file storage formats are defined as follows:
type tag storage format: 0, type, heat value;
subtype tag storage format: 1, subtype, heat value, type to which subtype belongs;
the master tag storage format: 2, lead actor, heat value;
director label storage format: 3, director, heat value;
the tag data storage formats are separated by a symbol | therebetween.
6. The method for recommending videos by internet television in a personalized manner according to claim 1, wherein the step 3) comprises: respectively generating a hot type label, a hot director label and a hot director label on the basis of the heat value of each label in the label database; wherein,
the generation process of the hot type label comprises the following steps: the method comprises the following steps of (1) taking type labels of the first two ranked heat values, and selecting one of the labels which is used relatively less as a hot type label according to the use records of the labels of the first two ranked heat values;
the generation process of the hot type label comprises the following steps: taking the sub-type label with the highest heat value under the hot type label as the hot sub-type label;
the generation process of the hot lead actor label: and taking the leading actor labels of the two first ranked heat values, and selecting and using a relatively small number of the leading actor labels as the hot actor labels according to the use records of the leading actor labels of the two first ranked heat values.
The generation process of the hot director label comprises the following steps: and taking the director labels of the first two ranked according to the heat value, and selecting and using one of the director labels as a hot director label according to the use records of the director labels of the first two ranked.
7. The method for personalized recommendation of video for internet tv as claimed in claim 1, wherein step 4) comprises screening out three popular programs:
(1) acquiring a program set list from a server side according to the popular type label and the popular subtype label, comparing historical records, removing the watched programs, and screening out eight programs according to the goodness of each program;
(2) acquiring a program set list from a server according to a popular lead actor tag, comparing historical records, removing watched programs, and screening out two programs according to the favorable evaluation degree of each program;
(3) acquiring a program set list from a server according to a hot director label, comparing historical records, removing watched programs, and screening out two programs according to the favorable evaluation degree of each program;
the three popular programs are combined to form the video program recommended to the user at one time.
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| CN107370827A (en) * | 2017-08-28 | 2017-11-21 | 四川长虹电器股份有限公司 | The system of active push personalized service |
| CN108897825A (en) * | 2018-06-21 | 2018-11-27 | 上海二三四五网络科技有限公司 | A kind of control method and control device updating films and television programs based on week group |
| CN109068185A (en) * | 2018-09-25 | 2018-12-21 | 湖南快乐阳光互动娱乐传媒有限公司 | Video screening method and system |
| CN110099296A (en) * | 2019-03-27 | 2019-08-06 | 维沃移动通信有限公司 | A kind of information display method and terminal device |
| CN110427500A (en) * | 2019-08-12 | 2019-11-08 | 浙江岩华文化传媒有限公司 | Information processing method, device and equipment |
| CN113127778A (en) * | 2021-03-17 | 2021-07-16 | 北京达佳互联信息技术有限公司 | Information display method and device, server and storage medium |
| CN113836355A (en) * | 2021-10-20 | 2021-12-24 | 盐城金堤科技有限公司 | Video recommendation method and device, computer storage medium and electronic equipment |
| CN116521985A (en) * | 2023-03-28 | 2023-08-01 | 深圳市一览网络股份有限公司 | Content recommendation method, system, equipment and storage medium |
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Application publication date: 20170531 |