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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- 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 present invention relates to a kind of internet television video broadcasting method.More particularly to a kind of internet television personalization is pushed away
The method for recommending video.
Background technology
Internet television is that, with the TV integrated machine or TV set-top box of internet content transmission channel, user can pass through
Video screen enjoys internet video content.Video recommendations mode be mostly it is non-personalized, impersonal theory recommend it is main with than
More single dimension goes to see global ranking plus half-life period, such as, ranking, one week popular ranking are clicked in 30 days.This non-
Propertyization recommends inefficiency, lack of targeted, it is likely that be not that user is interested, but also can cause Matthew effect, point
People is more, can be more by the people for recommending point, and powerhouse is stronger, and weak person's chance is more few weaker, and polarization may be caused serious,
Some comparing high-quality materials are just buried, in order to solve the problems, such as this Matthew effect, also mainly compliance dataization and automatic
Change pattern, it is necessary to increase personalized recommendation, personalized advantage is not only experienced, and considerably increases efficiency, allows use
Family quickly finds his thing interested.
The video recommendations mode of prior art is as shown in figure 1, in the data that can upload onto the server of the broadcasting record of user
The heart, by generating silver screen hits ranking list after data analysis, terminal device obtains ranking list data, shows use from server
Family is used as film recommendation.Prior art has as a drawback that
1st, background support is needed, the construction cycle is long, and cost is big；
2nd, go to see global ranking plus half-life period with relatively simple dimension, such as, ranking is clicked in 30 days, one week popular
Ranking, this impersonal theory recommends inefficiency, does not have specific aim, it is likely that be not user real interested, but also meeting
Cause Matthew effect, the people of point is more, can be more by the people for recommending point, powerhouse is stronger, and weak person's chance is more few weaker, causes
Polarization is serious, and some comparing high-quality materials are just buried.
The content of the invention
The technical problems to be solved by the invention are to provide a kind of personalized in the local internet television for completing by terminal
The method for recommending video.
The technical solution adopted in the present invention is：A kind of method of internet television personalized recommendation video, including it is as follows
Step：
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.
Step 1) described in user behavior, refer to concrete operations that user is carried out to video frequency program, when user is to a certain section
After mesh is operated, the tag attributes that the program possesses are obtained, the user behavior of collection includes：Into programme details page, play
Video, video-see are finished.
Program is to carry out labeling according to different attribute, and the label of program is summarized as four class label datas, including type, son
Type, protagonist and director；Wherein, video is divided into film, TV play, animation, variety, physical culture, science and education by type, under each type
There is respective subtype in face again.
Step 2) described in hot value refer to number of operations value of the user to similar label.
Step 2) include：
(1) user triggers the increase label hot value interface of tag database to the operation behavior of any sort label, increases
Label hot value interface increases the hot value of corresponding label in data buffer zone；
(2) the label data timing of data buffer zone is updated to file storage area, prevents the data in data buffer zone from losing
Lose；Specifically include：
Label data in data buffer zone, stores in file storage area, four class number of tags according to certain storage format
It is defined as follows according to file memory format：
Type label storage format：0, type, hot value；
Subtype label storage format：1, subtype, hot value, the type belonging to subtype；
Act the leading role label storage format：2, act the leading role, hot value；
Director's label storage format：3, director, hot value；
Symbol is used between label data storage format | separate.
Step 3) include：Based on the hot value of each label in tag database, popular type label, heat are generated respectively
Door subtype label, popular protagonist label and popular director's label；Wherein,
The generating process of popular type label：Take hot value and rank the type label of preceding two, according to ranking first two
The usage record of label, selection uses relatively little of one as popular type label；
The generating process of popular subtype label：It is heat to take the hot value highest subtype label under popular type label
Door subtype label；
Hot topic acts the leading role label generating process：The protagonist label that hot value ranks first two is taken, according to the seniority among brothers and sisters master of first two
The usage record of label is drilled, selection uses relatively little of one to act the leading role label as popular.
The generating process of hot topic director's label：Take hot value and rank director's label of preceding two, according to ranking first two
The usage record of label is directed, selection uses relatively little of one as popular director's label.
Step 4) including filtering out three kinds of popular programs：
(1) programme contribution list is obtained from server end according to popular type label and popular subtype label, contrasts history
Record, removes the program watched, and the favorable comment degree further according to each program filters out eight programs；
(2) label is acted the leading role according to hot topic and obtains programme contribution list from service end, contrast historical record, remove what is watched
Program, two programs are filtered out further according to each program favorable comment degree；
(3) programme contribution list is obtained from service end according to hot topic director's label, contrasts historical record, remove what is watched
Program, two programs are filtered out further according to each program favorable comment degree；
Above-mentioned three kinds of popular programs are combined as the video frequency program for once recommending user.
A kind of method of internet television personalized recommendation video of the invention, is locally completed, by analysis by terminal
The operation behavior of user, recommends the program that he may like, so as to realize personalized recommendation.Have the advantages that
1st, the background support of commending system is not needed, the construction cycle is short, low cost；
2nd, the personalized recommendation of record is browsed based on user, with very strong specific aim, user can be quickly found out sense
The film of interest, Consumer's Experience is significantly improved.
Brief description of the drawings
Fig. 1 is the video recommendation method schematic flow sheet of prior art；
Fig. 2 is a kind of flow chart of the method for internet television personalized recommendation video of the invention.
Specific embodiment
A kind of method of internet television personalized recommendation video of the invention is made with reference to embodiment and accompanying drawing
Describe in detail.
As shown in Fig. 2 a kind of method of internet television personalized recommendation video of the invention, comprises the following steps：
1) user behavior is collected, label data is obtained；
Described user behavior, refers to concrete operations that user is carried out to video frequency program, when user is carried out to certain program
After operation, the tag attributes that the program possesses are obtained, the user behavior of collection includes：Into programme details page, broadcasting video, regard
Frequency viewing is finished.Described program is to carry out labeling according to different attribute, and the label of program is summarized as four class label datas, bag
Include type, subtype, protagonist and director；Wherein, video is divided into film, TV play, animation, variety, physical culture, science and education by type,
There is respective subtype again below each type, such as the subtype of film is the story of a play or opera, love, action, comedy, terror, science fiction, outstanding
Doubtful, animation, war, magic, swordsman, record, ethics, micro- film.
2) label hot value in the tag database of each label of storage is updated, described hot value refers to user to same category
The number of operations value of label, the tag database is made up of two parts：Data buffer zone and file storage area；Including：
(1) user triggers the increase label hot value interface of tag database to the operation behavior of any sort label, increases
Label hot value interface increases the hot value of corresponding label in data buffer zone；
(2) the label data timing of data buffer zone is updated to file storage area, prevents the data in data buffer zone from losing
Lose；Specifically include：
Label data in data buffer zone, stores in file storage area, four class number of tags according to certain storage format
It is defined as follows according to file memory format：
Type label storage format：0, type, hot value；
Subtype label storage format：1, subtype, hot value, the type belonging to subtype；
Act the leading role label storage format：2, act the leading role, hot value；
Director's label storage format：3, director, hot value；
Symbol is used between label data storage format | separate, shape is such as：0, film, 13 | 0, TV play, 10 | 1, action, 13,
Film | 1, suspense, 2, film | 2, Tom, 4 | 3, Mike, 5.
3) according to data genaration hot topic label in tag database；Including：
Based on the hot value of each label in tag database, popular type label, popular subtype mark are generated respectively
Sign, popular protagonist label and hot topic direct label；Wherein,
The generating process of popular type label：Take hot value and rank the type label of preceding two, according to ranking first two
The usage record of label, selection uses relatively little of one as popular type label, prevents popular type label always same
It is individual；
The generating process of popular subtype label：It is heat to take the hot value highest subtype label under popular type label
Door subtype label；
Hot topic acts the leading role label generating process：The protagonist label that hot value ranks first two is taken, according to the seniority among brothers and sisters master of first two
The usage record of label is drilled, selection uses relatively little of one to act the leading role label as popular, prevents popular type label always same
One.
The generating process of hot topic director's label：Take hot value and rank director's label of preceding two, according to ranking first two
The usage record of label is directed, selection uses relatively little of one as popular director's label, prevents popular type label always
It is same.
4) video is recommended according to each popular label generation, including filters out three kinds of popular programs：
(1) programme contribution list is obtained from server end according to popular type label and popular subtype label, contrasts history
Record, removes the program watched, and the favorable comment degree further according to each program filters out eight programs；
(2) label is acted the leading role according to hot topic and obtains programme contribution list from service end, contrast historical record, remove what is watched
Program, two programs are filtered out further according to each program favorable comment degree；
(3) programme contribution list is obtained from service end according to hot topic director's label, contrasts historical record, remove what is watched
Program, two programs are filtered out further according to each program favorable comment degree；
Above-mentioned three kinds of popular programs are combined as the video frequency program for once recommending user.
Therefore, a kind of method of internet television personalized recommendation video of the invention does not need the big data branch of service end
Support, is locally completed by terminal, by analyzing the operation behavior of user, recommends the program that he may like, so as to realize individual
Propertyization is recommended.
Claims (7)
1. a kind of method of internet television personalized recommendation video, it is characterised in that comprise 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
Buffering area 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.
2. the method for a kind of internet television personalized recommendation video according to claim 1, it is characterised in that step 1)
Described user behavior, refers to concrete operations that user is carried out to video frequency program, after user operates to certain program, is obtained
The tag attributes that the program possesses are taken, the user behavior of collection includes：Finished watching into programme details page, broadcasting video, video observing
Finish.
3. the method for a kind of internet television personalized recommendation video according to claim 2, it is characterised in that program is
Labeling is carried out according to different attribute, the label of program is summarized as four class label datas, including type, subtype, act the leading role and lead
Drill；Wherein, video is divided into film, TV play, animation, variety, physical culture, science and education by type, has respective again below each type
Subtype.
4. the method for a kind of internet television personalized recommendation video according to claim 1, it is characterised in that step 2)
Described hot value refers to number of operations value of the user to similar label.
5. the method for a kind of internet television personalized recommendation video according to claim 1, it is characterised in that step 2)
Including：
(1) user triggers the increase label hot value interface of tag database to the operation behavior of any sort label, increases label
Hot value interface increases the hot value of corresponding label in data buffer zone；
(2) the label data timing of data buffer zone is updated to file storage area, prevents the loss of data in data buffer zone；Tool
Body includes：
Label data in data buffer zone, stores in file storage area according to certain storage format, four class label datas text
Part storage format is defined as follows：
Type label storage format：0, type, hot value；
Subtype label storage format：1, subtype, hot value, the type belonging to subtype；
Act the leading role label storage format：2, act the leading role, hot value；
Director's label storage format：3, director, hot value；
Symbol is used between label data storage format | separate.
6. the method for a kind of internet television personalized recommendation video according to claim 1, it is characterised in that step 3)
Including：Based on the hot value of each label in tag database, generate respectively popular type label, popular subtype label,
Hot topic acts the leading role label and popular director's label；Wherein,
The generating process of popular type label：The type label that hot value ranks first two is taken, according to the seniority among brothers and sisters label of first two
Usage record, selection use it is relatively little of one as hot topic type label；
The generating process of popular subtype label：It is popular son to take the hot value highest subtype label under popular type label
Type label；
Hot topic acts the leading role label generating process：The protagonist label that hot value ranks first two is taken, according to the seniority among brothers and sisters protagonist mark of first two
The usage record of label, selection uses relatively little of one to act the leading role label as popular.
The generating process of hot topic director's label：Director's label that hot value ranks first two is taken, according to the seniority among brothers and sisters director of first two
The usage record of label, selection uses relatively little of one as popular director's label.
7. the method for a kind of internet television personalized recommendation video according to claim 1, it is characterised in that step 4)
Including filtering out three kinds of popular programs：
(1) programme contribution list is obtained from server end according to popular type label and popular subtype label, contrasts historical record,
Remove the program watched, the favorable comment degree further according to each program filters out eight programs；
(2) label is acted the leading role according to hot topic and obtains programme contribution list from service end, contrast historical record, remove the section watched
Mesh, two programs are filtered out further according to each program favorable comment degree；
(3) programme contribution list is obtained from service end according to hot topic director's label, contrasts historical record, remove the section watched
Mesh, two programs are filtered out further according to each program favorable comment degree；
Above-mentioned three kinds of popular programs are combined as the video frequency program for once recommending user.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 | A kind of video screening technique 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 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102236669A (en) * | 2010-04-30 | 2011-11-09 | 腾讯科技（深圳）有限公司 | Recommendation list generation method, device, media server, client and method |
| CN103136275A (en) * | 2011-12-02 | 2013-06-05 | 盛乐信息技术（上海）有限公司 | System and method for recommending personalized video |
| CN104394471A (en) * | 2014-11-19 | 2015-03-04 | 四川长虹电器股份有限公司 | Method for intelligently recommending favorite program to user |
| CN104731950A (en) * | 2015-03-31 | 2015-06-24 | 北京奇艺世纪科技有限公司 | Video recommendation method and device |
| CN105095431A (en) * | 2015-07-22 | 2015-11-25 | 百度在线网络技术（北京）有限公司 | Method and device for pushing videos based on behavior information of user |
-
2016
- 2016-12-14 CN CN201611156617.8A patent/CN106792172A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102236669A (en) * | 2010-04-30 | 2011-11-09 | 腾讯科技（深圳）有限公司 | Recommendation list generation method, device, media server, client and method |
| CN103136275A (en) * | 2011-12-02 | 2013-06-05 | 盛乐信息技术（上海）有限公司 | System and method for recommending personalized video |
| CN104394471A (en) * | 2014-11-19 | 2015-03-04 | 四川长虹电器股份有限公司 | Method for intelligently recommending favorite program to user |
| CN104731950A (en) * | 2015-03-31 | 2015-06-24 | 北京奇艺世纪科技有限公司 | Video recommendation method and device |
| CN105095431A (en) * | 2015-07-22 | 2015-11-25 | 百度在线网络技术（北京）有限公司 | Method and device for pushing videos based on behavior information of user |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 | A kind of video screening technique 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 |
| CN113127778B (en) * | 2021-03-17 | 2023-10-03 | 北京达佳互联信息技术有限公司 | Information display method, 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 |
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Application publication date: 20170531 |