CN105224576A - A kind of video display intelligent recommendation method - Google Patents
A kind of video display intelligent recommendation method Download PDFInfo
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- CN105224576A CN105224576A CN201410310368.8A CN201410310368A CN105224576A CN 105224576 A CN105224576 A CN 105224576A CN 201410310368 A CN201410310368 A CN 201410310368A CN 105224576 A CN105224576 A CN 105224576A
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Abstract
The invention discloses a kind of video display intelligent recommendation method, described video display intelligent recommendation method comprises and obtains data to set up video display metadatabase from live broadcast system and internet, obtain user data and set up customer data base, the basis of video display metadatabase and customer data base is recommended out according to the socialgram of user and interest figure the video display list matched with user interest.Video display intelligent recommendation method provided by the invention, obtain data by employing from live broadcast system and internet set up video display metadatabase and obtain user data, based on interest figure and the socialgram of video display metadatabase and user, for user provide just the programme televised live of hot broadcast, most popular request program with and similar program and the personalized recommendation of program that mates with user interest, achieve the intelligent recommendation of video display, be very easy to user and carry out video display viewing, improve viewing experience.
Description
Technical field
The present invention relates to television technology field, particularly relate to a kind of video display intelligent recommendation method.
Background technology
Along with the development of time, since occurring, emerged uncountable video display from video display, rhythm of life of today is than very fast, and people are in the mode a kind of leisure being selected to loosen after work, and reasonable mode appreciates video display exactly.But how watching from the vast video display as selected oneself favorite huge and voluminous video display storehouse, obtaining best viewing experience, having become the problem that is extremely urgent; If oneself go to find, check, looking in so many video display is the very large work of quantities, and in the life that rhythm is fast, no one is willing to spend this time to go to find.Thus, a kind of method needing intelligent video display to recommend, can recommend out video display fast, is convenient for people to life.
Summary of the invention
In view of current television technology field above shortcomings, the invention provides a kind of video display intelligent recommendation method, can intelligent recommendation video display, be that user finds popular and the most personalized movie and television contents.
For achieving the above object, embodiments of the invention adopt following technical scheme:
A kind of video display intelligent recommendation method, described video display intelligent recommendation method comprises video display intelligent recommendation method following steps:
Obtain data from live broadcast system and internet and set up video display metadatabase;
Obtain user data and set up customer data base;
Video display metadatabase and customer data base basis are recommended out according to the socialgram of user and interest figure the video display list matched with user interest;
The video display list that broadcast interface shows recommendation selects viewing for user.
According to one aspect of the present invention, described step obtains data from live broadcast system and internet and can be to the embodiment setting up video display metadatabase: obtain internet data, third party degree of depth EPG data and live EPG data, to form unified polymerization storehouse and tag library to set up video display metadatabase.
According to one aspect of the present invention, the specific implementation that the polymerization storehouse that described formation is unified and tag library set up video display metadatabase can be:
The data obtained from different data source enter interim polymerized data base;
Carry out the structural data parsing processing of content metadata, program label data;
Data after resolving processing are carried out standardization processing and audited to form video display metadatabase.
According to one aspect of the present invention, described video display intelligent recommendation method is further comprising the steps of: according to the association program request of the tag attributes of video display metadatabase content and live formation focus, popular video display list.
According to one aspect of the present invention, described step can comprise the following steps according to the tag attributes association program request of video display metadatabase content and live formation focus, popular video display list:
Structural data is processed;
Content recommendation is recombinated;
Associate management is carried out to content;
Participle management is carried out to program;
Carry out tag control;
Issue recommendation results.
According to one aspect of the present invention, describedly processing is carried out to structural data be specially: after obtaining content recommendation data, and strategy can be represented carry out recombining contents according to difference sequence.
According to one aspect of the present invention, describedly associate management is carried out to content be specially: after completing labeling process, system carries out auto-associating according to preset strategy.
According to one aspect of the present invention, described to program carry out participle management be specially: analyzing and processing is carried out to the text of program metadata, provides and text sequence is cut into independent entry.
According to one aspect of the present invention, described step recommends the video display list embodiment matched to user interest to be according to the socialgram of user and interest figure on video display metadatabase with the basis of customer data base: by gathering user and to like the behavior of program and evaluating, the historical information of context information that real-time collecting is relevant and passing user, analysis excavate the demand of user under current residing sight condition and extract the recommendation that kinsfolk's interest characteristics carries out programme content.
According to one aspect of the present invention, described video display intelligent recommendation method is further comprising the steps of: timing automatic upgrades metadata, supplementary and enhancing.
Advantage of the invention process: video display intelligent recommendation method of the present invention obtains data by employing from live broadcast system and internet and sets up video display metadatabase and obtain user data, then on the basis of video display metadatabase and customer data base, recommend out according to the socialgram of user and interest figure match with user interest video display list, the method of watching is selected in the last video display list showing recommendation on broadcast interface for user, based on interest figure and the socialgram of video display metadatabase and user, for user provides just at the programme televised live of hot broadcast, most popular request program with and similar program and the personalized recommendation of program that mates with user interest, achieve the intelligent recommendation of video display, be very easy to user and carry out video display viewing, improve viewing experience.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, be briefly described to the accompanying drawing used required in embodiment below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is a kind of video display intelligent recommendation method schematic diagram described in the embodiment of the present invention one;
Fig. 2 is a kind of video display intelligent recommendation method schematic diagram described in the embodiment of the present invention two.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Embodiment one
As shown in Figure 1, a kind of video display intelligent recommendation method, described video display intelligent recommendation method comprises the following steps:
Step S1: obtain data from live broadcast system and internet and set up video display metadatabase;
Described step S1 obtains data from live broadcast system and internet and can be to the embodiment setting up video display metadatabase: obtain internet or access third party's degree of depth EPG data and live EPG data, forms unified polymerization storehouse and tag library to set up video display metadatabase.
Wherein, unified polymerization storehouse is formed and tag library can be to the specific implementation step setting up video display metadatabase:
The data obtained from different data source enter interim polymerized data base;
Carry out the structural data parsing processing of content metadata, program label data;
Data after resolving processing are carried out standardization processing and audited to form video display metadatabase.
Its content sources mainly comprises internet, mobile phone, PAD, TV end, User Defined content and third party partner content.Classifying content comprises the live content of the on-demand contents such as film, TV play, variety, animation, documentary film and CCTV, provincial satellite TV and some local broadcasting stations.
And in actual applications, obtain by reptile and data mining technology from the video website of main flow, social networks, film review website and search engine web site, analyze data, thus obtain abundant internet video display metadata, live EPG data are obtained from live broadcast system, carry out filtering, mate after obtain degree of depth EPG data, import third party degree of depth EPG data again, three carries out coupling combination and sets up unified polymerization storehouse and tag library, forms video display metadatabase.
Step S2: obtain user data and set up customer data base;
The embodiment that described step S2 acquisition user data sets up customer data base is: import user's master data, described master data comprises the information such as sex, age, region/geographic position, but is not limited only to these information; Import user behavior data, described behavioral data comprises the information such as viewing history, social circle, hobby of user, but is not limited only to these information.
Step S3: recommend out the video display list matched with user interest on the basis of video display metadatabase and customer data base according to the socialgram of user and interest figure;
Described step S3 recommends the embodiment of the video display list matched to user interest to be according to the socialgram of user and interest figure on video display metadatabase with the basis of customer data base: by gathering user and to like the behavior of program and evaluating, the historical information of context information that real-time collecting is relevant and passing user, analysis excavate the demand of user under current residing sight condition and extract the recommendation that kinsfolk's interest characteristics carries out programme content, the contradiction between solving that content is infinitely abundant and liking with user individual.Based on user's historical data generating recommendations data, extract kinsfolk's interest characteristics, recommend out the film list of mating with user interest.Learn the interest characteristics of user each in family and classify, finding the large class of several interest that can attract member in this family, be mapped to Video clustering space, being sorted by space length metric algorithm obtains optimum recommended program list.
In actual applications, each user has oneself a database, comprises " interest figure " data (user may see tens, upper tuber of stemona film) of user and " socialgram " data (user may have tens, the virtual community of up to a hundred good friends and activity thereof).Thus in use, user ID here can be newly-built logon account in system, it also can be the logon account (such as Sina's microblogging, Tengxun's microblogging, Renren Network etc.) of existing social networks; Can be single individuality, also can be one family (can identify kinsfolk's interest characteristics separately).
Wherein carrying out recommendation according to socialgram can comprise following several: friend recommendation, and friend recommendation is to my content; Good friend is seeing, the content that good friend is watching or seeing, and further expands to the video that on social networks, good friend sees/comments on; Microblogging is paid close attention to, video (past 24 hours or arbitrarily fixed time section, proprietary concern etc.) of greatest concern on microblogging; Congenial to one's tastes: user clustering, for user recommends to possess with it video that same interest feature customer group likes.
Carry out recommending concrete enforcement to comprise according to interest figure: according to the activity recommendation of the information customization personalizations such as domestic consumer, individual member, regional location, time period, use habit; According to member individual in domestic consumer watch custom and corresponding individualized time section combination mode recommend, such as set the program that some time periods recommend to be suitable for a certain position individual member hobby, a certain class program can be set and only recommend user in certain period of every day, can be set in certain certain time, recommend the content of specifying to user.
Step S4: the video display list showing recommendation on broadcast interface selects viewing for user.
In actual applications, also step can be automatically performed according to actual change process: video display metadatabase is upgraded, supplement and strengthens.
Embodiment two
As shown in Figure 2, a kind of video display intelligent recommendation method, described video display intelligent recommendation method comprises the following steps:
Step S1: obtain data to set up video display metadatabase from live broadcast system and internet;
Described step S1 obtains data from live broadcast system and internet and can be to the embodiment setting up video display metadatabase: obtain internet or access third party's degree of depth EPG data and live EPG data, forms unified polymerization storehouse and tag library to set up video display metadatabase.
Wherein, unified polymerization storehouse is formed and tag library can be to the specific implementation step setting up video display metadatabase:
The data obtained from different data source enter interim polymerized data base;
Carry out the structural data parsing processing of content metadata, program label data;
Data after resolving processing are carried out standardization processing and audited to form video display metadatabase.
Its content sources mainly comprises internet, mobile phone, PAD, TV end, User Defined content and third party partner content.Classifying content comprises the live content of the on-demand contents such as film, TV play, variety, animation, documentary film and CCTV, provincial satellite TV and some local broadcasting stations.
And in actual applications, obtain by reptile and data mining technology from the video website of main flow, social networks, film review website and search engine web site, analyze data, thus obtain abundant internet video display metadata, live EPG data are obtained from live broadcast system, carry out filtering, mate after obtain degree of depth EPG data, import third party degree of depth EPG data again, three carries out coupling combination and sets up unified polymerization storehouse and tag library, forms video display metadatabase.
Step S2: obtain user data and set up customer data base;
The embodiment that described step S2 acquisition user data sets up customer data base is: import user's master data, described master data comprises the information such as sex, age, region/geographic position, but is not limited only to these information; Import user behavior data, described behavioral data comprises the information such as viewing history, social circle, hobby of user, but is not limited only to these information.
Step S3: recommend out the video display list matched with user interest on the basis of video display metadatabase and customer data base according to the socialgram of user and interest figure;
Described step S3 recommends the embodiment of the video display list matched to user interest to be according to the socialgram of user and interest figure on video display metadatabase basis: by gathering user and to like the behavior of program and evaluating, the historical information of context information that real-time collecting is relevant and passing user, analysis excavate the demand of user under current residing sight condition and extract the recommendation that kinsfolk's interest characteristics carries out programme content, the contradiction between solving that content is infinitely abundant and liking with user individual.Based on user's historical data generating recommendations data, extract kinsfolk's interest characteristics, recommend out the film list of mating with user interest.Learn the interest characteristics of user each in family and classify, finding the large class of several interest that can attract member in this family, be mapped to Video clustering space, being sorted by space length metric algorithm obtains optimum recommended program list.
In actual applications, each user has oneself a database, comprises " interest figure " data (user may see tens, upper tuber of stemona film) of user and " socialgram " data (user may have tens, the virtual community of up to a hundred good friends and activity thereof).Thus in use, user ID here can be newly-built logon account in system, it also can be the logon account (such as Sina's microblogging, Tengxun's microblogging, Renren Network etc.) of existing social networks; Can be single individuality, also can be one family (can identify kinsfolk's interest characteristics separately).
Wherein carrying out recommendation according to socialgram can comprise following several: friend recommendation, and friend recommendation is to my content; Good friend is seeing, the content that good friend is watching or seeing, and further expands to the video that on social networks, good friend sees/comments on; Microblogging is paid close attention to, video (past 24 hours or arbitrarily fixed time section, proprietary concern etc.) of greatest concern on microblogging; Congenial to one's tastes: user clustering, for user recommends to possess with it video that same interest feature customer group likes.
Carry out recommending concrete enforcement to comprise according to interest figure: according to the activity recommendation of the information customization personalizations such as domestic consumer, individual member, regional location, time period, use habit; According to member individual in domestic consumer watch custom and corresponding individualized time section combination mode recommend, such as set the program that some time periods recommend to be suitable for a certain position individual member hobby, a certain class program can be set and only recommend user in certain period of every day, can be set in certain certain time, recommend the content of specifying to user.
Step S4: the video display list showing recommendation on broadcast interface selects viewing for user.
In actual applications, also step can be automatically performed according to actual change process: video display metadatabase is upgraded, supplement and strengthens.
Step S5: according to the association program request of the tag attributes of video display metadatabase content and live formation focus, popular video display list;
Described step S5 according to the embodiment of the association program request of the tag attributes of video display metadatabase content and live formation focus, popular video display list is: the metadata, EPG data etc. that read video display metadatabase, proposed algorithm can carry out pre-service to metadata, each film is produced to the list of an association, often have new data to add, algorithm is wanted automatically to carry out incremental computations and association results to be upgraded.The algorithm factor simultaneously participating in calculating can adjust flexibly, and can support that simple insertion recommends adjustment.Specific implementation function is as follows:
Structural data is processed: the structural data of content metadata, program label data etc. resolves processing.
Content recommendation is recombinated: after obtaining content recommendation data, can sort according to difference and represent strategy and carry out recombining contents.
Associate management is carried out to content: after completing labeling process, system carries out robotization association according to preset strategy.
Participle management is carried out to program: analyzing and processing is carried out to the text of program metadata, provides and text sequence is cut into independent entry, coordinate with intelligent recommendation engine and raise the efficiency and accuracy.
Carry out tag control: the tag control function of internet and third party's data access, tag class can flexible configuration, and label data constantly can expand according to new content.
Issue recommendation results: carry out association analysis according to the current live content of watching of user or the on-demand content checked, recommending data is provided.
Advantage of the invention process: video display intelligent recommendation method of the present invention obtains data by employing from live broadcast system and internet and sets up video display metadatabase and obtain user data, then on video display metadatabase and customer data base basis, recommend out according to the socialgram of user and interest figure match with user interest video display list, the method of watching is selected in the last video display list showing recommendation on broadcast interface for user, based on interest figure and the socialgram of video display metadatabase and user, for user provides just at the programme televised live of hot broadcast, most popular request program with and similar program and the personalized recommendation of program that mates with user interest, achieve the intelligent recommendation of video display, be very easy to user and carry out video display viewing, improve viewing experience.
The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, the technician of any skilled is in technical scope disclosed by the invention; the change that can expect easily or replacement, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of described claim.
Claims (10)
1. a video display intelligent recommendation method, is characterized in that, described video display intelligent recommendation method comprises the following steps:
Obtain data from live broadcast system and internet and set up video display metadatabase;
Obtain user data and set up customer data base;
The basis of video display metadatabase and customer data base is recommended out according to the socialgram of user and interest figure the video display list matched with user interest;
The video display list that broadcast interface shows recommendation selects viewing for user.
2. video display intelligent recommendation method according to claim 1, it is characterized in that, described step obtains data from live broadcast system and internet and can be to the embodiment setting up video display metadatabase: obtain internet data, third party degree of depth EPG data and live EPG data, to form unified polymerization storehouse and tag library to set up video display metadatabase.
3. video display intelligent recommendation method according to claim 2, is characterized in that, the specific implementation that the polymerization storehouse that described formation is unified and tag library set up video display metadatabase can be:
The data obtained from different data source enter interim polymerized data base;
Carry out the structural data parsing processing of content metadata, program label data;
Data after resolving processing are carried out standardization processing and audited to form video display metadata
Storehouse.
4. video display intelligent recommendation method according to claim 1, is characterized in that, described video display intelligent recommendation method is further comprising the steps of: according to the association program request of the tag attributes of video display metadatabase content and live formation focus, popular video display list.
5. video display intelligent recommendation method according to claim 4, is characterized in that, described step can comprise the following steps according to the tag attributes association program request of video display metadatabase content and live formation focus, popular video display list:
Structural data is processed;
Content recommendation is recombinated;
Associate management is carried out to content;
Participle management is carried out to program;
Carry out tag control;
Issue recommendation results.
6. video display intelligent recommendation method according to claim 5, is characterized in that, described content recommendation restructuring is specially: after obtaining content recommendation data, can sort according to difference and represent strategy and carry out recombining contents.
7. video display intelligent recommendation method according to claim 5, is characterized in that, described relevance management is specially: after completing labeling process, system carries out auto-associating according to preset strategy.
8. video display intelligent recommendation method according to claim 5, is characterized in that, described program participle management is specially: carry out analyzing and processing to the text of program metadata, provide and text sequence is cut into independent entry.
9. video display intelligent recommendation method according to claim 1, it is characterized in that, described step recommends the video display list embodiment matched to user interest to be according to the socialgram of user and interest figure on video display metadatabase with the basis of customer data base: by gathering user and to like the behavior of program and evaluating, the historical information of context information that real-time collecting is relevant and passing user, analysis excavate the demand of user under current residing sight condition and extract the recommendation that kinsfolk's interest characteristics carries out programme content.
10. according to the video display intelligent recommendation method one of claim 1 to 9 Suo Shu, it is characterized in that, described video display intelligent recommendation method is further comprising the steps of: timing automatic upgrades metadata, supplementary and enhancing.
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