CN103024585B - Program recommendation system, program recommendation method and terminal equipment - Google Patents
Program recommendation system, program recommendation method and terminal equipment Download PDFInfo
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Abstract
The invention is applicable to the technical field of communication and provides a program recommendation system, a program recommendation method and terminal equipment. The program recommendation system comprises a MongoDB cloud database, a Hadoop file processor, a Mahout library and a Web server. The MongoDB cloud database is used for storing program data, identity information data of users and data information of programs historically browsed by users; the Handoop file processor is used for synchronizing data and information stored in the MongoDB cloud database; the Web server is used for receiving program recommendation requests of users to call the Mahout library and further used for displaying recommended programs through Web services; and the Mahout library is used for calling the MongoDB cloud database, selecting and starting recommendation modes based on content or users according to types of the recommendation requests of users, and matching recommended program information according to the recommendation modes. Program recommendation according to characteristics of users and programs is realized to enable user's favorite programs to be quickly searched out from massive program data.
Description
Technical field
The invention belongs to communication technique field, more particularly, to a kind of program recommendation system, method and terminal unit.
Background technology
With emerging in multitude of smart electronicses product, the intelligent terminal's mobile phone headed by Fructus Mali pumilae and Google, flat board fill in a large number
Scold market, caused the arrival in intelligent epoch afterwards, domestic television producer builds the intelligent terminal with android as system one after another
Product, and develop the application program of many on product.This makes the video display trend with intelligent television as characteristic will get over
More to become trend of new generation.With constantly weeding out the old and bring forth the new of various TV programme and movie program, how to pass through terminal unit
The program data being quickly found out needs in the program data of magnanimity becomes problem demanding prompt solution.
If prior art user terminal unit to be passed through obtains oneself program data interested, base in mass data
Originally it is to pass through some simple condition manual search by user to obtain programme information that may be interested, such mode obtains
Programme information compares limitation, and a lot of program datas are all invalid data, and search procedure is complicated, search time is long.
Content of the invention
The purpose of the embodiment of the present invention is to provide a kind of program recommendation system, method and terminal unit it is intended to solve existing
The programme information that the terminal unit program data acquisition methods having technology obtain compares limitation, and a lot of program datas are all invalid
Data, and search procedure complexity, the problem of search time length.
To achieve these goals, the embodiment of the present invention provides following technical scheme：
The embodiment of the present invention is achieved in that a kind of program recommendation system, and described system includes：
MongoDB cloud data base, browses for storaging program data, the identity information data of user and user's history
Program data information；
Hadoop file handler, the data for synchronous described MongoDB cloud database purchase and information；
Web server, the program recommendation request of receive user, call Mahout storehouse；
Mahout storehouse, calls through the synchronous MongoDB cloud data base of described Hadoop file handler, described
In MongoDB cloud data base, according to user's recommendation request type, select to start content-based recommendation or pushing away based on user
Recommend pattern, and according to described recommendation pattern, mate user's programme information interested, and to described programme information at
Reason, generates and recommends program；
Described Web server, is additionally operable to generate by Web service and recommend program.
The embodiment of the present invention additionally provides a kind of terminal unit, and described terminal unit includes above-mentioned program recommendation system.
The embodiment of the present invention additionally provides a kind of TV programme suggesting method methods described and includes：
Receive program recommendation request；
According to described program recommendation request, judge the type of program recommendation request；
Select the program giving based on user characteristicses to recommend and/or the program based on content characteristic is recommended.
The embodiment of the present invention compared with prior art, has the beneficial effects that：By being divided using distribution examination data base MongoDB
Every lane database in cloud for the cloth, using Hadoop distributed document processing module Lai the data of synchronous MongoDB, and
Spring AOP is configured on MongoDB data base, Spring AOP is mainly used to processing data inquiry transaction, MongoDB data
Data in the middle of storehouse can be indicated by JSON form, calls backstage commending system by web server it is recommended that system is adjusted
With the Mahout storehouse on backstage, realize proposed algorithm, MongoDB data can be inquired about in the meantime, with JSON form, return request and ring
Should give user, realize carrying out program recommendation according to user's self-characteristic and program characteristics to user so that can be in magnanimity program
Find the program that user likes in data, search procedure is simple, search speed is fast, and recommend to be finally reached independent of flat
Platform, magnanimity disposal ability, high reliability, the program recommendation effect of high fault-tolerant ability.
Brief description
In order to be illustrated more clearly that the technical scheme of the embodiment of the present invention, below will be to required use in embodiment description
Accompanying drawing be briefly described it should be apparent that, drawings in the following description are only some embodiments of the present invention, for this
For the those of ordinary skill of field, on the premise of not paying creative work, can also be obtained other according to these accompanying drawings
Accompanying drawing.
Fig. 1 is the structure chart of the program recommendation system that the embodiment of the present invention one provides；
Fig. 2 is the message processing flow figure of Web server in the embodiment of the present invention two program commending method；
Fig. 3 is Mahout response recommended flowsheet figure in the embodiment of the present invention two program commending method；
Fig. 4 is MongDB response recommended flowsheet figure in the embodiment of the present invention two program commending method；
Fig. 5 is the systematic collaboration management process in figure embodiment two program commending method of the present invention；
Fig. 6 is the flow chart of the TV programme suggesting method that the embodiment of the present invention three provides；
Fig. 7 is the flow chart that in Fig. 6 of the present invention, the program based on user characteristicses is recommended；
Fig. 8 is the schematic diagram of the ultimate principle of the program recommendation mechanisms in Fig. 6 of the present invention based on user characteristicses.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, below in conjunction with drawings and Examples, right
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only in order to explain the present invention, and
It is not used in the restriction present invention.
MongoDB cloud data base, browses for storaging program data, the identity information data of user and user's history
Program data information；
Hadoop file handler, the data for synchronous described MongoDB cloud database purchase and information；
Web server, the program recommendation request of receive user, call Mahout storehouse, realize program and recommend；
Mahout storehouse, calls through the synchronous MongoDB cloud data base of described Hadoop file handler, described
In MongoDB cloud data base, according to user's recommendation request type, select to start content-based recommendation or pushing away based on user
Recommend pattern, and according to described recommendation pattern, user's programme information interested is mated by Mahout storehouse, and to described program
Information is processed, and generates and recommends program；
Described Web server, is additionally operable to generate by Web service and recommend program.
The embodiment of the present invention also correspondingly provides a kind of terminal unit, described terminal unit and above-mentioned program recommendation system
Set up network connection, receive media content recommendations, and realize interacting with user, play or display media content.
The embodiment of the present invention additionally provides a kind of TV programme suggesting method methods described and includes：
Receive program recommendation request；
According to described program recommendation request, judge the type of program recommendation request；
Select the program giving based on user characteristicses to recommend and/or the program based on content characteristic is recommended.
Below in conjunction with specific embodiment, the realization of the present invention is described in detail：
Embodiment one
The present embodiment provides a kind of program recommendation system, and this system, with technological frame Mahout, based on Hadoop, is passed through
WebServices provides service for Web server, and user sends the request of media recommender, web by the browser of intelligent television
The video display recommended engine of server calls the system, video display recommended engine calls Mahout to recommend framework, finally calls MongoDB
Obtain shadow recommending data.Simultaneously in order to process mass data and synchronous each server cluster, calculated distributed using Hadoop cloud
Framework by each for data syn-chronization sub-cluster, sub-cluster using synchronous come data genaration video display recommendation tables.Final realization is passed through
Semen Caesalpiniae existing incremental data storage process, Distributed Calculation and synchronization, realize video display recommended engine by the API that Mahout provides,
There is provided service by web services, below program recommendation system of the present invention is specifically described：
Fig. 1 shows the structure chart of the program recommendation system that the embodiment of the present invention one provides, and for convenience of description, only illustrates
The part related to the embodiment of the present invention, this device can be built in terminal unit software unit, hardware cell or
Person's soft or hard combining unit.
Described system includes：MongoDB cloud data base 11, Hadoop file handler 12, Web server 13, Mahout
Storehouse 14, the system facilitates mass data distributed storage, synchronous and transmission, analysis mining using cloud computing technology；
MongoDB cloud data base 11, clear for storaging program data, the identity information data of user and user's history
The program data information look at；
Hadoop file handler 12, the data for synchronous described MongoDB cloud database purchase and information；
Web server 13, for the program recommendation request of receive user, calls the proposed algorithm in Mahout storehouse to realize media
Recommend；
In the present embodiment, the use of web services is for the puppy parc based on http using web services, is taken using web
Business, it accesses and can access more convenient independent of specific terminal unit or server apparatus, and WebServices is application journey
Sequence assembly, is communicated using open protocol, WebServices be independent (self-contained) and can self portrait,
By using universal description, find with integrated (Universal Description, Discovery and Integration,
UDDI) finding, in addition, WebServices can be used by other servers or application program, XML is the base of WebServices
Plinth.When building and using WebService, mainly use technology and the rule of following key:
1.XML:The standard method of description data；
2.SOAP:Simple Object Access Protocol；
3.WSDL:WSDL；
4.UDDI：Be a kind of independent of platform, based on XML language for describing the agreement of commercial affairs on the internet.
Mahout storehouse 14, calls the MongoDB cloud data base 11 through the synchronization of described Hadoop file handler 12, in institute
State in MongoDB cloud data base 11, according to user's recommendation request type, select to start content-based recommendation or be based on user
Recommendation pattern, and according to described recommendation pattern, mate user's programme information interested, and described programme information carried out
Process, generate and recommend program；
The machine learning framework that Mahout is, it is achieved that many recommend basic algorithm, by using it, can complete to push away
Recommend the realization held up, Mahout calls storehouse as the core of proposed algorithm here.
Described Web server 13, is additionally operable to generate by Web service and recommend program.
Optionally, described system also includes at least one backup server, stores described MongoDB cloud data for backup
Data in storehouse 11 and information, and monitor described MongoDB cloud data base 11, the number in described MongoDB cloud data base 11
According to and during information updating, renewal is synchronized to the data and information of home server storage, and works as described MongoDB cloud number
When breaking down according to storehouse 11, provide data and information by backup server.
In the present embodiment, by backup server, the data in MongoDB cloud data base 11 and information are backed up, standby
The number of part server can be set as needed.
Optionally, described system is additionally included in setting Data buffer in MongoDB cloud data base 11, for buffer-stored
Recommend program data.
It is contemplated that data base generally there are in hard disk in the present embodiment, inquiry velocity is slow, and data volume is big, can be by user
The result of request, is specially put into a Buffer Pool, is stored the result of recommendation in the way of internal memory, this Buffer Pool can deposit many parts
Recommendation results, and be temporally index with enquiry frequency, delete earliest inquiry and inquiry frequency from Buffer Pool according to Preset Time
Rate comes last request results.
Optionally, described system adopts asynchronous distribution agreement, including：The label different to the Data Identification of transmission, according to
Described label judges the corresponding event type of each data, with parallel, different events is processed.
In the present embodiment, in order to process many places Asynchronous Request, system sets up asynchronous distribution agreement, according to asynchronous distribution agreement
Process the request in many places strange land, the data of actual transmissions can be divided into several classes:
Judge user's request in this way, stamp request label req；
Judge user response in this way, stamp responsive tags res；
Judge database synchronization request in this way, stamp synchronization request label dataSyn；
The information data that every a transmission is come, responds corresponding data manipulation by judging tag types, to determine to connect
By still respond request, or synchrodata, when there being multiple request, first distribute the request to the clothes of same database support
In the middle of business device, unnecessary ranks, one by one process request, and multiple response databases are synchronously similar, here due to employing
The mode of different labels, makes server can realize asynchronism with concurrent processing different event, due to distribution mechanisms and queuing mechanism
Process so that multi-user request response and synchronous event efficiently and reliably execute.
Optionally, described Web server provides Simple Object Access Protocol (Simple Object Access
Protocol, SOAP) interface, by described SOAP interface, the media push of compatible with various terminals equipment.
Optionally, in order to obtain more preferable recommendation effect, system concurrency lock can be set up, for the request of user, realize
Multipath concurrence accesses, and for same request, we lock for same movie data result, and the priority by user's request
Order is ranked, and properly processes user's request.
Embodiment two
The present embodiment is program commending method embodiment, the information of Web server in described program commending method embodiment
Process chart, as shown in Fig. 2 details are as follows：
In S201, user sends video display request by browser；
In S202, web server calls intelligent video display recommended engine；
In S203, described intelligent video display recommended engine is using recommending framework Mahout to be recommended, wherein, described
Mahout is recommended according to the data in the synchronous cloud data base MongoDB of Hadoop.
In the present embodiment, Mahout storehouse responds program recommended flowsheet figure, as shown in figure 3, details are as follows：
In S301, reception system request message, call the data in MongoDB data base；
In S302, by Hadoop file handler, the data in described MongoDB data base is synchronized, be worth
Illustrate is that this synchronizing process can carry out data syn-chronization by clock triggering；
In S303, according to the data in described MongoDB data base, intelligent video display recommended engine calls Mahout to generate
Recommend video display；
In S304, described recommendation video display are supplied to by terminal by web server.
In the present embodiment, the MongoDB response recommended flowsheet figure of program commending method, as shown in figure 4, details are as follows：
In S401, receive the data mining analysis request that Mahout database data is recommended；
In S402, according to described request, generate corresponding table, described table includes：Obtain and recommend video display table, related video display
List and and user behavior table information；
In S403, described table is verified；
In S404, according to the table by verification, execution recommending or the recommendation based on video display based on user.
In the present embodiment, program commending method systematic collaboration management process, as shown in figure 5, details are as follows：
In S501, obtain movie data；
In S502, according to recommendation request, carry out data mapping, and enter according to data genaration respective table or to user behavior
Row calculates and forms recommendation tables；
In S503, receive user program data request, start nomination process, generate the recommendation program for user terminal
Data；
In S504, described recommendation program data is recommended user.
Program recommendation system in the present embodiment, using distributed data base MongoDB distribution every service beyond the clouds
In device, using Hadoop distributed document processor Lai the data of synchronous MongoDB, and on MongoDB data base configure
Spring AOP, Spring AOP is mainly used to processing data inquiry transaction, and the data in the middle of MongoDB data base can be passed through
JSON form is indicated, and calls backstage commending system by web server it is recommended that system calls the Mahout storehouse on backstage, real
Existing proposed algorithm, can inquire about MongoDB data in the meantime, with JSON form, return request and respond to user, realize according to
Family self-characteristic and program characteristics carry out program recommendation to user so that can find what user liked in magnanimity program data
Program, search procedure is simple, search speed is fast, and recommends to be finally reached independent of platform, magnanimity disposal ability, Gao Ke
Program recommendation effect by property, high fault-tolerant ability.
In addition, the beneficial effect of the embodiment of the present invention also includes：
1st, using based on cloud computing framework Hadoop and distributed data base MongoDB, beneficial to process magnanimity movie data
And user behavior data, data deposits in high in the clouds, and extensibility is strong, and disposal ability is strong.
2nd, the system utilizes the SOA system of the service-oriented body industry structure of WebServices, and versatility is higher, can be independent
In platform, operating system, versatility is higher.Either mobile phone, TV, PC or other networking gear, can obtain identical
Video display push away effect.
3rd, the framework based on Mahout for the system, the mode based on movie data and user behavior of realizing carries out video display and pushes away
Recommend, for user behavior, generate user's row table and table family recommendation tables, mapping, formation characteristic table and shadow are passed through for movie data
Depending on table, then generate video display recommendation tables.For specific user behavior information, here can be according to the difference of user type, to not
Same user profile is accompanied by different weights, and the difference of such weighted value is different to no user's meaning, thus recommend
Video effect has more specific aim.The system can obtain one or more video display and recommend simultaneously, and carries out ranking, thus reaching more
Good recommendation effect.
4th, using concurrently lock, asynchronous distribution agreement, Buffer Pool, many backups and the mechanism such as re-transmission of shaking hands, realize highly reliable, high
Effectively, high fault-tolerantly, support that multi-user both can concurrently and can be asynchronously in local and high in the clouds recommendation movie data technical scheme.
Embodiment three
On the basis of embodiment two, for example, to described program commending method systematic collaboration management process further
Optimize, the flow chart that Fig. 6 shows the realization of TV programme suggesting method that the embodiment of the present invention three provides, details are as follows：
In S601, receive program recommendation request；
In the present embodiment, S601 can be accomplished by：
Judge user's request in this way, stamp request label req；
Judge user response in this way, stamp responsive tags res；
Judge database synchronization request in this way, stamp synchronization request label dataSyn；
The information data that every a transmission is come, responds corresponding data manipulation by judging tag types.
By parallel processing is carried out to different requests and response data, the processing speed of data can be improved, thus more
The fast programme content to user's recommendation request.
In S602, according to described program recommendation request, judge the type of program recommendation request；
In S603, select the program giving based on user characteristicses to recommend and/or the program based on content characteristic is recommended.
In the present embodiment, optionally, the program based on user characteristicses is recommended to specifically include, and refers to Fig. 7：
In S701, obtain the identity data of user and the programme information of historical viewings；
In S702, according to the programme information of described identity data and historical viewings, generate user behavior table, described user
Behavior table includes the attribute information of the attribute information, terminal device information and historical viewings program of user itself；
In S703, according to described behavior table, coupling user program interested in the program database prestoring
Information, described program database includes content information and the program attribute information of each program；
It is recommended that exporting described programme information in S704.
Further, in order to more accurately recommend program data to user, S703 is specifically permissible：By in user behavior table
The attribute information of the attribute information of user itself and historical viewings program configures different priority；Now, S704 is specially：Press
According to the behavior table of described priority, in the program database prestoring, coupling user programme information interested.
In order to make it easy to understand, below with the analogy that is recommended as of article, introducing the principle based on user recommends and said
Bright, refer to the schematic diagram of the ultimate principle of the recommendation mechanisms that Fig. 8 is based on user it is assumed that user A likes articles for use A, articles for use C,
User B likes articles for use B, user C to like articles for use A, articles for use C and articles for use D；From the history preference information of these users, Wo Menke
It is that ratio is relatively similar with the taste of discovery user A and user C and preference, user C also likes articles for use D simultaneously, then we are permissible
Infer that user A may also like articles for use D, therefore articles for use D can be recommended user A.
In the present embodiment, optionally, the described program recommendation step based on content characteristic, including：In advance to programme content
Carry out classifying by characteristic type, and priority weighting is marked to characteristic type, base is carried out according to classifying content and priority weighting
Program in content is recommended.Wherein, described characteristic type can be the information such as the country of program, type, leading role, time span.
In order to make it easy to understand, following, with a concrete implementation example, to the present embodiment, the program based on content is recommended to carry out
Illustrate, but the process of realizing or not example with this is limited：
It is exemplified below the video display table of existing following film information：
Film A (001, transformer, Japan, animation, 2007-01-02)
Film B (002, three states, China, history, 2008)
Film C (003, Wall Street, the U.S., finance)
According to described programme information, following property list will be generated, wherein, secondary series be weights, the 3rd row be country, the 4th
Row are movie name.
| 001 | 1 | j | Transformer |
| 002 | 2 | c | Three states |
| 003 | 3 | a | Wall Street |
Described video display table and property list are all stored in the middle of MongoDB in the way of data base.
In recommendation process, with country as priority, with weights as condition, inquire about MongoDB data base, obtain data base
Data in property list, returning result collection resultA, then utilize resultA, by video display table, inquire about video display title, interior
Hold, and the time etc..Obtaining video display Query Result resultB, being used it is recommended that giving according to the result formation recommendation tables of resultB return
Family.
Further, select to give the program recommendation and/or the recommendation of the program based on content characteristic based on user characteristicses
During, after obtaining program to be recommended, can also be according to character similarity algorithm, program interested in described user
It is ranked up, thus providing the user the program data more meeting its demand.
In addition, in order to more pointedly recommend program data to user：Can also be according to the sex of user, age, kind
The size of race, culture, the time seen a film, the occupation of user, the subject matter of video display, the length of content, and TV equipment, screen
Resolution etc. is assigned to different weights, and different attributes is set as with different priority groups, and the information of such as user itself is permissible
It is set to a weights group, and setting priority, a weights group can be set to the information of equipment, and setting priority, for video display
Information can set a weights level, and setting priority, then set up the according to priority vertical recommended models with power, user carried out
The good user profile targetedly recommended, come simultaneously for interpolation, constantly updates user's list item so that recommendation results, is more suitable for
User.
In the present embodiment, receive program recommendation request, according to described program recommendation request, judge the class of program recommendation request
Type, selects the program giving based on user characteristicses to recommend and/or the program based on content characteristic is recommended, and realizes according to user itself
Characteristic and program characteristics carry out program recommendation to user and like so that user can quickly be searched in magnanimity program data
Program, search procedure is simple, search speed is fast.
It should be noted that in said system embodiment, included unit simply carries out drawing according to function logic
Point, but it is not limited to above-mentioned division, as long as being capable of corresponding function；In addition, each functional unit is concrete
Title also only to facilitate mutual distinguish, is not limited to protection scope of the present invention.
In addition, one of ordinary skill in the art will appreciate that realizing all or part of step in the various embodiments described above method
The program that can be by complete come the hardware to instruct correlation, and corresponding program can be stored in an embodied on computer readable storage and be situated between
In matter, described storage medium, such as ROM/RAM, disk or CD etc..
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.
Claims (10)
1. a kind of program recommendation system is it is characterised in that described system includes：
MongoDB cloud data base, the section browsing for storaging program data, the identity information data of user and user's history
Mesh data message；
Hadoop file handler, the data for synchronous described MongoDB cloud database purchase and information；
Web server, the program of receive user recommends Asynchronous Request, calls Mahout storehouse；
Mahout storehouse, calls through the synchronous MongoDB cloud data base of described Hadoop file handler, in described MongoDB
In cloud data base, according to user's recommendation request type, select to start content-based recommendation or the recommendation pattern based on user,
And according to described recommendation pattern, mate user's programme information interested, and described programme information is processed, generation pushes away
Recommend program；
Described Web server, is additionally operable to generate by Web service and recommend program；
Described system adopts asynchronous distribution agreement, according to the request in asynchronous distribution protocol processes many places strange land, including：To transmission
The different label of Data Identification, judges user's request in this way, stamps request label req, judges user response in this way, stamp response
Label res, judges database synchronization request in this way, stamps synchronization request label dataSyn, judge each number according to described label
According to corresponding event type, with parallel, different events is processed.
2. the system as claimed in claim 1, it is characterised in that described system also includes at least one backup server, is used for
Backup stores data and information in described MongoDB cloud data base, and monitors described MongoDB cloud data base, when described
When the data in MongoDB cloud data base and information updating, renewal is synchronized to the data and information of home server storage,
And when described MongoDB cloud data base is broken down, provide data and information by backup server.
3. the system as claimed in claim 1 is it is characterised in that described system also includes Data buffer, for buffer-stored
Recommend program data.
4. the system as claimed in claim 1 is it is characterised in that described Web server provides simple object access protocol
Interface, by described SOAP interface, the media push of compatible with various terminals equipment.
5. a kind of terminal unit is it is characterised in that described terminal unit is included described in Claims 1-4 any claim
Program recommendation system.
6. a kind of TV programme suggesting method is it is characterised in that methods described includes：
The program of Web server receive user recommends Asynchronous Request；
Asynchronous Request is recommended according to described program, judges that program recommends the type of Asynchronous Request；
Call the synchronous MongoDB cloud data base of Hadoop file handler by calling Mahout storehouse, in described MongoDB cloud
Select the program giving based on user characteristicses to recommend in data base and/or the program based on content characteristic is recommended；
Wherein, described reception program recommends Asynchronous Request step to include：
Judge user's request in this way, stamp request label req；
Judge user response in this way, stamp responsive tags res；
Judge database synchronization request in this way, stamp synchronization request label dataSyn；
The information data that every a transmission is come, responds corresponding data manipulation by judging tag types, with parallel to not
Same event is processed.
7. method as claimed in claim 6 is it is characterised in that the described program based on user characteristicses is recommended to include step：
Obtain the identity data of user and the programme information of historical viewings；
According to the programme information of described identity data and historical viewings, generate user behavior table, described user behavior table includes using
The attribute information of the attribute information at family itself, terminal device information and historical viewings program；
According to described behavior table, coupling user programme information interested, described program in the program database prestoring
Data base includes content information and the program attribute information of each program；
Recommend to export described programme information.
8. method as claimed in claim 7 is it is characterised in that the described program according to described identity data and historical viewings is believed
Breath, generates user behavior table step and is specially：
By priority different for the attribute information configuration of the attribute information of user itself and historical viewings program in user behavior table；
Described according to described behavior table, in the program database prestoring, coupling user's programme information interested is concrete
For：
According to the behavior table of described priority, in the program database prestoring, coupling user programme information interested.
9. method as claimed in claim 6 is it is characterised in that the described program recommendation step based on content characteristic, including：
In advance programme content is carried out classifying by characteristic type, and priority weighting is marked to characteristic type；
Recommended according to the program that classifying content and priority weighting are carried out based on content.
10. method as claimed in claim 6 it is characterised in that described selection give based on user characteristicses program recommend and/
Or the program recommendation based on content characteristic includes：
According to character similarity algorithm, program interested in described user is ranked up.
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| CN113298112B (en) * | 2021-04-01 | 2023-05-16 | 安徽继远软件有限公司 | Integrated data intelligent labeling method and system |
| CN113384898B (en) * | 2021-06-10 | 2024-01-30 | 网易（杭州）网络有限公司 | Data processing method, device, equipment and storage medium |
| CN115510155A (en) * | 2022-09-22 | 2022-12-23 | 中国船舶集团有限公司第七一一研究所 | Data dynamic synchronization method and edge user terminal |
| CN116437159B (en) * | 2023-03-14 | 2024-08-23 | 深圳感臻智能股份有限公司 | Data processing method, system and medium based on digital television protocol |
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