CN111836078A - Content recommendation method based on user behavior analysis and applied to traditional set top box - Google Patents
Content recommendation method based on user behavior analysis and applied to traditional set top box Download PDFInfo
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- CN111836078A CN111836078A CN202010728020.6A CN202010728020A CN111836078A CN 111836078 A CN111836078 A CN 111836078A CN 202010728020 A CN202010728020 A CN 202010728020A CN 111836078 A CN111836078 A CN 111836078A
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- content recommendation
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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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
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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
-
- 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
Abstract
The invention relates to a content recommendation method based on user behavior analysis and applied to a traditional set top box, which comprises the following steps: s1, the set-top box acquires user operation information and program information to be watched and uploads the information to the server; s2, the server records the operation information of the user and scores the operation items corresponding to each operation information; s3, generating UI presentation configuration data for each user according to the scores, and displaying the recommended online again by the users; s4, providing service items at the background according to the program information uploaded by the set top box to classify the users; and S5, generating a user demand tendency report according to the classification of the user, and recommending the program to the user. The recommendation method can provide content production direction and data support for service operation, accurately recommend service content concerned by each user, and also provide data support for service operation; in the aspect of value added services, accurate service content recommendation can greatly attract users to order corresponding value added services.
Description
Technical Field
The invention relates to the field of set top boxes, in particular to a content recommendation method based on user behavior analysis and applied to a traditional set top box.
Background
The set-top box has become an information window of a common family living room after years of development, and not only watches satellite broadcast television programs, but also is an information window of a communication internet. At present, the mainstream of the market is also based on an embedded traditional set top box, and the traditional set top box is communicated with the Internet.
Based on the internet, a set-top box manufacturer can provide wider service contents, such as a series of value-added services like IPTV, OTT, advertisement, subscription information, and the like.
At present, relevant services provided by manufacturers are uniform for terminal users, and personalized services cannot be provided according to specific user cases; the method collects and analyzes the user preference by establishing the user behavior, and displays the individual recommended content to each specific terminal user.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a content recommendation method based on user behavior analysis, which is applied to a traditional set top box.
The technical scheme adopted by the invention for solving the technical problems is as follows: a content recommendation method based on user behavior analysis applied to a traditional set top box is constructed, and the method comprises the following steps:
s1, the set-top box acquires user operation information and program information to be watched and uploads the information to the server;
s2, the server records the operation information of the user and scores the operation items corresponding to each operation information;
s3, generating UI presentation configuration data for each user according to the scores, and displaying the recommended online again by the users;
s4, providing service items at the background according to the program information uploaded by the set top box to classify the users;
and S5, generating a user demand tendency report according to the classification of the user, and recommending the program to the user.
Preferably, in step S1, the operation items corresponding to the operation information items are numbered, and the item numbers corresponding to each operation information item of the user are uploaded to the server.
Preferably, the program information includes at least one of EPG information, channel type, program information, and user usage value added service information of the program.
Preferably, in step S2, the server establishes a database for each user; and recording user operation information in real time, and grading and storing each operation item.
Preferably, in the step S2, the score is 80% of the actual operating frequency of the user + 20% of the operation and maintenance recommendation trend.
Preferably, in step S4, the sorting criterion is 20% + (program information index + value added service information index) 40% + regional time period index 20% + operation and maintenance recommendation trend 20%.
Preferably, the indices are counted by the viewing frequency of the user.
Preferably, the set-top box updates the user content recommendation configuration periodically.
The content recommendation method based on the user behavior analysis applied to the traditional set top box has the following beneficial effects: the content recommendation method based on user behavior analysis applied to the traditional set top box can provide content production direction and data support for service operation, realize thousands of people for terminal operation and service, accurately recommend the service content concerned by each user, and simultaneously provide data support for service operation; based on the content recommendation of the user using behaviors, the user can form great approval on the set top box product, and the sale of the set top box product is promoted; in the aspect of value added services, accurate service content recommendation can greatly attract users to subscribe corresponding value added services.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic flow structure diagram of a content recommendation method based on user behavior analysis applied to a conventional set top box in an embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1, a content recommendation method based on user behavior analysis applied to a conventional set-top box in a preferred embodiment of the present invention includes the following steps:
s1, the set-top box acquires user operation information and program information to be watched and uploads the information to the server;
s2, the server records the operation information of the user and scores the operation items corresponding to each operation information;
s3, generating UI presentation configuration data for each user according to the scores, and displaying the recommended online again by the users;
s4, providing service items at the background according to the program information uploaded by the set top box to classify the users;
and S5, generating a user demand tendency report according to the user data, and recommending programs to the user.
The content recommendation method based on user behavior analysis applied to the traditional set top box can provide content production direction and data support for service operation, realize thousands of people for terminal operation and service, accurately recommend the service content concerned by each user, and simultaneously provide data support for service operation; based on the content recommendation of the user using behaviors, the user can form great approval on the set top box product, and the sale of the set top box product is promoted; in the aspect of value added services, accurate service content recommendation can greatly attract users to subscribe corresponding value added services.
Further, in step S1, the operation items corresponding to the operation information items are numbered, and the item numbers corresponding to the operation information items of the user are uploaded to the server.
Further, the Program information includes at least one of EPG (Electronic Program Guide) information of the Program, a channel type, Program information, and value added service information used by the user.
Further, in step S2, the server establishes a database for each user; and recording user operation information in real time, and grading and storing each operation item.
Further, in step S2, the score is 80% of the actual operating frequency of the user + 20% of the operation and maintenance recommendation trend.
The generated UI presentation configuration data can be classified according to users, and the requirement tendency of the users can be displayed more intuitively.
Further, in step S4, the sorting criterion ═ channel type index × (program information index + value added service information index) × 40% + regional period index × 20% + operation and maintenance recommendation tendency × 20%.
Further, in step S5, a user service demand tendency report can be generated by performing simple data mining based on the user data
Furthermore, each index is counted by the watching frequency of the user, the set top box updates the content recommendation configuration of the user periodically according to the statistical data, and content recommendation is carried out according to the latest behavior analysis of the user.
For example: a user in a Brazilian time zone regularly watches live Brazilian football league of a sports table at eight night, and can preferentially recommend Brazilian football related videos, Brazilian sports related videos and football related videos … in other areas to the user according to an existing resource library in the OTT service of an operation and maintenance system, so that the recommended content of the user is generated.
It is to be understood that the above-described respective technical features may be used in any combination without limitation.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. A content recommendation method based on user behavior analysis applied to a traditional set top box is characterized by comprising the following steps:
s1, the set-top box acquires user operation information and program information to be watched and uploads the information to the server;
s2, the server records the operation information of the user and scores the operation items corresponding to each operation information;
s3, generating UI presentation configuration data for each user according to the scores, and displaying the recommended online again by the users;
s4, providing service items at the background according to the program information uploaded by the set top box to classify the users;
and S5, generating a user demand tendency report according to the classification of the user, and recommending the program to the user.
2. The content recommendation method based on user behavior analysis as claimed in claim 1, wherein in step S1, the operation items corresponding to the operation information are numbered, and the item numbers corresponding to each operation information of the user are uploaded to the server.
3. The content recommendation method based on user behavior analysis applied to the legacy set-top box according to claim 1, wherein the program information comprises at least one of EPG information, channel type, program information of the program, and information of value added service used by the user.
4. The content recommendation method based on user behavior analysis as claimed in claim 1, wherein in step S2, the server establishes a database for each user; and recording user operation information in real time, and grading and storing each operation item.
5. The content recommendation method based on user behavior analysis as claimed in claim 4, wherein in step S2, the rating is 80% of the actual operating frequency of the user + 20% of the recommended trend of the operation and maintenance.
6. The method for recommending content based on user behavior analysis as claimed in claim 3, wherein in said step S4, the sorting criterion is 20% + (program information index + value added service information index) 40% + regional time period index 20% + operation and maintenance recommendation trend 20%.
7. The content recommendation method based on user behavior analysis applied to conventional set-top boxes of claim 6, wherein each index is counted by user watching frequency.
8. The content recommendation method based on user behavior analysis applied to traditional set-top boxes according to any of claims 1 to 7, wherein the set-top box updates the user content recommendation configuration periodically.
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| CN202010728020.6A CN111836078A (en) | 2020-07-23 | 2020-07-23 | Content recommendation method based on user behavior analysis and applied to traditional set top box |
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| CN109087171A (en) * | 2018-06-15 | 2018-12-25 | 长沙市到家悠享家政服务有限公司 | Service merchant recommendation method, device and electronic equipment |
| US20190018557A1 (en) * | 2017-07-13 | 2019-01-17 | Spotify Ab | System and method for steering user interaction in a media content environment |
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2020
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Patent Citations (6)
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| CN103164192A (en) * | 2011-12-08 | 2013-06-19 | 上海未来宽带技术股份有限公司 | Individuation interface forming method and system for set top box |
| US8775165B1 (en) * | 2012-03-06 | 2014-07-08 | Google Inc. | Personalized transliteration interface |
| CN106503006A (en) * | 2015-09-07 | 2017-03-15 | 阿里巴巴集团控股有限公司 | The sort method and device of application App neutron applications |
| CN105677721A (en) * | 2015-12-30 | 2016-06-15 | 深圳创维数字技术有限公司 | User interaction interface recommendation method and system based on set top box |
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