CN106412700A - Intelligent television power-on channel recommendation method - Google Patents
Intelligent television power-on channel recommendation method Download PDFInfo
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- CN106412700A CN106412700A CN201610945336.4A CN201610945336A CN106412700A CN 106412700 A CN106412700 A CN 106412700A CN 201610945336 A CN201610945336 A CN 201610945336A CN 106412700 A CN106412700 A CN 106412700A
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- channel
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- intelligent television
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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
- H04N21/45—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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The invention relates to the field of intelligent television personalized recommendation, and discloses an intelligent television power-on channel recommendation method so as to perform personalized recommendation when an intelligent television is powered on. The intelligent television power-on channel recommendation method comprises the steps of acquiring live channel watching behavior data of a user to a big data platform; fabricating tags for favorite live channels of the user; calculating the television channel watching time length recently of the user according to the behavior data and the tags, and generating a channel list of the favorite channels of the user; loading the generated channel list to a database, and loading the channel list to a search server from the database; acquiring image data of the favorite channels of the user, and sending the image data to a message system; distributing user favorite channel data to a television by the message system according to a user mac address; performing comparison by the television after being powered on, acquiring a favorite channel of the user, and switching to the channel. The intelligent television power-on channel recommendation method is applicable to intelligent televisions.
Description
Technical field
The present invention relates to intelligent television personalized recommendation field, the method recommended particularly to intelligent television startup channel.
Background technology
The appearance of internet and popularize and bring substantial amounts of information to user, the upsurge of big data forces enterprise to pass through to excavate
User-dependent data, to analyze the hobby of user, by understanding the hobby of user, designs and can more preferably meet different levels
The product of user's request.User-customized recommended application is a relatively good application meeting the needs of different users.It is according to
The information requirement at family, interest etc., information interested for user, product etc. are recommended user.Carry out personalized calculating, by user
Corelation behaviour data finds the point of interest of user, thus guiding the user discover that the information requirement of oneself.One good personalization pushes away
Recommend can not only provide the user personalization service moreover it is possible to set up substantial connection and user between, allow user to recommend produce according to
Rely.Personalized recommendation has been widely used in a lot of fields, wherein most typically have the neck of good development and application prospect
Domain is exactly e-commerce field.
Content of the invention
The technical problem to be solved in the present invention is：There is provided a kind of method that intelligent television startup channel is recommended, when intelligent electricity
After startup channel start, carry out realizing personalized recommendation.
For solving the above problems, the technical solution used in the present invention is：The method that intelligent television startup channel is recommended, including
Following steps：
A. collection user watches the behavioral data of direct broadcast band to big data platform；
B. make the label of user live broadcast favorite channel；
C. nearest a period of time viewing television channel of user according to described behavioral data and described label, is calculated by channel
Duration, take the viewing the longest favorite channel the most of duration, the channel table of last output user's favorite channel；
D. the channel table of output is loaded in database, then is loaded into search server from database；
E. the API calling search server writes interface, obtains the favorite channel representation data of user, is sent to message system
System；
F. message system by user's favorite channel data according to user's mac address distribution to TV；
G., after TV gets this data, put into buffer area；
H. user start, TV obtain user's favorite channel data cached, by the current available machine time with data cached enter
Row compares, and obtains the favorite channel of user of time period at that time, and TV automatically switches this channel.
Further, described behavioral data includes：Mac address, television channel, TV programme, enter channel time, exit
Channel time, acquisition time
Further, described big data platform is hadoop big data platform, and described channel table is hive table, described data
Storehouse is hbase database.
Further, described search server is elasticsearch search server.
The invention has the beneficial effects as follows：The behavioral data based on the viewing television channel of user for the present invention, builds user and divides
The favorite channel user portrait of period, according to the period channel portrait of user, each user's started television automatically switches to user
The channel liked, the therefore present invention disclosure satisfy that the demand of different user, can by information interested for user, product exactly
Recommend user.
Specific embodiment
Describe technical scheme with reference to embodiment in detail：
A kind of method that intelligent television startup channel is recommended is it is characterised in that the favorite channel building user time-sharing section is used
Draw a portrait in family, according to the period channel portrait of user, each user's started television automatically switches to the channel that user likes.Specifically such as
Under：
1. dispose acquisition platform, collection user watches the behavioral data of direct broadcast band to hadoop big data platform, gathers
Key message include：Mac address, television channel, TV programme, enter channel time, exit channel time, acquisition time；
2. label is specifically divided into three-level, is respectively by designing user live favorite channel label：One-level, working day, non-
Working day；Two grades, 24 hourly averages in a day are divided into 12 time periods (0-2,2-4,4-6 ...), three-level, user's favorite channel
Title；
3. the design according to user behavior data and label, is calculated the nearest one week viewing television channel of user by channel
Duration, takes viewing duration favorite channel the most the longest, last output user's favorite channel hive table, such as user A is in work
Make day 0-2 point and have viewed CCTV1 50 minutes, CCTV5 30 minutes, then the favorite channel of 0-2 point is user on weekdays
CCTV1；
4. the hive table of user's favorite channel label of output is loaded into hbase database, then adds from hbase database
It is loaded into elasticsearch search server；
5. the API calling elasticsearch search server writes interface, obtains the favorite channel portrait number of user
According to being sent to message system；
6. message system by user's favorite channel data according to user's mac address distribution to TV；
7., after TV gets this data, put into buffer area；
8. user's start, it is data cached, by current available machine time, date and caching number that TV obtains user's favorite channel
According to comparing, obtain the favorite channel of user of time period at that time, TV automatically switches this channel, such as when user A again
If TV is seen in the start of 0-2 point on weekdays, TV automatically switches to CCTV1.
The foregoing describe the general principle of the present invention and main feature, the description of specification is to illustrate that the present invention's is former
Reason, without departing from the spirit and scope of the present invention, the present invention also has various changes and modifications, these changes and improvements
Both fall within scope of the claimed invention.
Claims (4)
1. the method that intelligent television startup channel is recommended is it is characterised in that comprise the steps：
A. collection user watches the behavioral data of direct broadcast band to big data platform；
B. make the label of user live broadcast favorite channel；
C. according to described behavioral data and described label, calculate by channel user nearest a period of time viewing television channel when
Long, take viewing duration favorite channel the most the longest, the channel table of last output user's favorite channel；
D. the channel table of output is loaded in database, then is loaded into search server from database；
E. the API calling search server writes interface, obtains the favorite channel representation data of user, is sent to message system；
F. message system by user's favorite channel data according to user's mac address distribution to TV；
G., after TV gets this data, put into buffer area；
H. user's start, TV acquisition user's favorite channel is data cached, is compared with data cached by the current available machine time
Right, obtain the favorite channel of user of time period at that time, TV automatically switches this channel.
2. intelligent television startup channel as claimed in claim 1 is recommended method is it is characterised in that described behavioral data bag
Include：Mac address, television channel, TV programme, enter channel time, exit channel time, acquisition time.
3. the method that intelligent television startup channel as claimed in claim 1 is recommended is it is characterised in that described big data platform is
Hadoop big data platform, described channel table is hive table, and described database is hbase database.
4. the method that intelligent television startup channel as claimed in claim 1 is recommended is it is characterised in that described search server is
Elasticsearch search server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610945336.4A CN106412700A (en) | 2016-10-26 | 2016-10-26 | Intelligent television power-on channel recommendation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610945336.4A CN106412700A (en) | 2016-10-26 | 2016-10-26 | Intelligent television power-on channel recommendation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106412700A true CN106412700A (en) | 2017-02-15 |
Family
ID=58013655
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610945336.4A Pending CN106412700A (en) | 2016-10-26 | 2016-10-26 | Intelligent television power-on channel recommendation method |
Country Status (1)
| Country | Link |
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| CN (1) | CN106412700A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107241641A (en) * | 2017-08-10 | 2017-10-10 | 四川长虹电器股份有限公司 | The method of the most frequently used channel of set top box automatic switchover |
| 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 |
| CN107391752A (en) * | 2017-08-16 | 2017-11-24 | 四川长虹电器股份有限公司 | A kind of method based on hadoop platform construction user tag information |
| CN110830844A (en) * | 2019-11-20 | 2020-02-21 | 四川长虹电器股份有限公司 | Intelligent pushing method for television terminal |
| CN112969077A (en) * | 2021-03-19 | 2021-06-15 | 世子合德(杭州)网络科技有限公司 | Novel live system is sold to network |
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| CN1606746A (en) * | 2000-11-22 | 2005-04-13 | 皇家菲利浦电子有限公司 | Television program recommender with interval-based profiles for determining time-varying conditional probabilities |
| CN102170590A (en) * | 2010-02-26 | 2011-08-31 | 康佳集团股份有限公司 | Intelligent television control system and control method |
| CN103051960A (en) * | 2011-10-13 | 2013-04-17 | 纬创资通股份有限公司 | Television program recommendation system and method thereof |
| US20140130075A1 (en) * | 2004-04-07 | 2014-05-08 | Jun Yabe | Information processing apparatus and method, computer program thereof, and recording medium |
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2016
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Patent Citations (4)
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| CN1606746A (en) * | 2000-11-22 | 2005-04-13 | 皇家菲利浦电子有限公司 | Television program recommender with interval-based profiles for determining time-varying conditional probabilities |
| US20140130075A1 (en) * | 2004-04-07 | 2014-05-08 | Jun Yabe | Information processing apparatus and method, computer program thereof, and recording medium |
| CN102170590A (en) * | 2010-02-26 | 2011-08-31 | 康佳集团股份有限公司 | Intelligent television control system and control method |
| CN103051960A (en) * | 2011-10-13 | 2013-04-17 | 纬创资通股份有限公司 | Television program recommendation system and method thereof |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107241641A (en) * | 2017-08-10 | 2017-10-10 | 四川长虹电器股份有限公司 | The method of the most frequently used channel of set top box automatic switchover |
| CN107391752A (en) * | 2017-08-16 | 2017-11-24 | 四川长虹电器股份有限公司 | A kind of method based on hadoop platform construction user tag information |
| 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 |
| CN110830844A (en) * | 2019-11-20 | 2020-02-21 | 四川长虹电器股份有限公司 | Intelligent pushing method for television terminal |
| CN112969077A (en) * | 2021-03-19 | 2021-06-15 | 世子合德(杭州)网络科技有限公司 | Novel live system is sold to network |
| CN112969077B (en) * | 2021-03-19 | 2021-11-23 | 世子合德(杭州)网络科技有限公司 | Novel live system is sold to network |
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Application publication date: 20170215 |