CN106792171A - Personalized recommendation method and system in intelligent video app - Google Patents
Personalized recommendation method and system in intelligent video app Download PDFInfo
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- CN106792171A CN106792171A CN201611154179.1A CN201611154179A CN106792171A CN 106792171 A CN106792171 A CN 106792171A CN 201611154179 A CN201611154179 A CN 201611154179A CN 106792171 A CN106792171 A CN 106792171A
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- personalized 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]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
- H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
-
- 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
-
- 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
- H04N21/47—End-user applications
- H04N21/478—Supplemental services, e.g. displaying phone caller identification, shopping application
- H04N21/47815—Electronic shopping
-
- 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]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/81—Monomedia components thereof
- H04N21/812—Monomedia components thereof involving advertisement data
Abstract
Personalized recommendation method in a kind of intelligent video app, including：S0, configuration video app monitoring threads, and the priority of video app monitoring threads is set to highest so that video app monitoring threads continuous service in mobile terminal；S1, the personal information for obtaining user in video app；S2, when video is played out in video app, according to the type information of video tab information acquisition video, and monitored by video app and obtain the video information；S3, the circle of friends information that user is obtained according to the personal information of user in video app；Personal information according to user predicts the preliminary video preference information of user；S4, the Preliminary List that video personalized recommendation is obtained by the type information of the video in step S2；S5, the alternate list of video personalized recommendation screen by the emotion preference of user in step S3 obtain final video personalized recommendation list；Final video personalized recommendation list is shown on video app.
Description
Technical field
The present invention relates to personalized recommendation technical field, personalized recommendation method in more particularly to a kind of intelligent video app
And system.
Background technology
Internet is the important channel that people obtain information, and the user that is mainly characterized by of conventional internet finds certainly
, it is necessary to carry out substantial amounts of search by browser during oneself things interested, while needing artificially to filter out a large amount of uncorrelated
Result, it is cumbersome, and expend time and efforts.
By taking ecommerce as an example, the user that is mainly characterized by of traditional electronic commerce logs in each e-commerce platform, seeks
The commodity that oneself is interested are looked for, is then bought.But with the continuous expansion of ecommerce scale, businessman passes through shopping network
Stand there is provided substantial amounts of commodity, user is quickly understood all of commodity, also cannot directly check the quality of commodity, user
Requiring a great deal of time can just find the commodity of oneself needs with energy.It is this to browse a large amount of unrelated information and product mistake
Journey, can undoubtedly make the consumer that is submerged in magnanimity information because without discovery oneself needs or commodity interested without
Cutout is lost, and the time cost for not only making user do shopping is significantly increased, while making the conversion of the commodity purchasing of e-commerce platform
Rate is very low.
In the prior art, it is proposed that according to user's purchase or navigation patterns, feature is carried out with a certain proposed algorithm
The personalized recommendation method of information (such as merchandise news), for example, being clustered to books according to books classification, content first, builds
Vertical books cluster system, such as A and B is a class books.Then according to the navigation patterns and purchaser record of user, analysis user's sense
It is that A or history bought A that interest books, such as user browse commodity at present, you can by similar B book recommendations to A.
But being rested on existing personalized recommendation method based on article, the individual character based on user or based on collaborative filtering more
Change recommendation method.It is not suitable for the recommendation of personalization in video app.
The content of the invention
In view of this, it is individual in the intelligent video app of personalized recommendation a kind of app suitable for intelligent video of present invention proposition
Propertyization recommends method and system.
Personalized recommendation method in a kind of intelligent video app, it comprises the following steps：
S0, configuration video app monitoring threads, and the priority of video app monitoring threads is set to highest so that video
App monitoring threads continuous service in mobile terminal；The video app monitoring threads be used for record video app the startup time,
Run time, end run time information；And startup time, run time, the information life of end run time according to video app
Into video app moving law charts；
S1, the personal information for obtaining user in video app；
S2, when video is played out in video app, according to the type information of video tab information acquisition video, and lead to
Cross video app and monitor pause, retroversion, F.F. information in the playing duration information, the playing process that obtain the video；
S3, the circle of friends information that user is obtained according to the personal information of user in video app；According to the personal information of user
Predict the preliminary video preference information of user；The dominant expression information of user is extracted from the circle of friends information of user, from dominant
Analysis obtains the emotion preference of user in expressing information；
S4, the Preliminary List that video personalized recommendation is obtained by the type information of the video in step S2, by video
Playing duration information, playing process in pause, fall back, F.F. information filters to the Preliminary List of video personalized recommendation
Obtain the alternate list of video personalized recommendation；
S5, the alternate list of video personalized recommendation is screened most by the emotion preference of user in step S3
Whole video personalized recommendation list；
S6, judge whether video app starts, directly show final video personalized recommendation on video app on startup
List；When not actuated, the video app moving laws chart in step S0 shows on mobile terminal informing and finally regards
Frequency personalized recommendation list.
In personalized recommendation method in intelligent video app of the present invention,
It also comprises the following steps：
The click of S7, acquisition user for the video in final video personalized recommendation list, broadcasting record, and measuring point
Hit, play record, optimized for the video personalized recommendation list to next time.
In personalized recommendation method in intelligent video app of the present invention,
The step S1 includes：
Obtain the privacy settings information of userspersonal information of the user on video app；Judged by privacy settings information
Whether there is the authority of the userspersonal information for obtaining user on video app；When with authority, user is obtained in video
Userspersonal information on app；The personal information includes dominant information, recessive information and the friendship network letter of user
Breath；The dominant information of the user includes age, sex, the occupational information of user；The recessive information of the user includes user
Video click information.
In personalized recommendation method in intelligent video app of the present invention,
Broadcast information weights are set in the step S2；Broadcast information weights are 1 plus when the playing duration of video and video
Ratio long；
Monitor pause, the reverse information of user；When pause, the reverse information of user is got, extract pause when and
The first video frame information before and after play time after retroversion in the range of the scheduled time；
The content information of the first video frame information is obtained by graphical analysis, the content information of the first video frame information is made
It is positive content information；
Monitor the F.F. information of user；When the F.F. information of user is got, extract F.F. and start in termination interval
The second video frame information；
The content information of the second video frame information is obtained by graphical analysis, the content information of the second video frame information is made
It is negative sense content information；
And the positive content information of storage frame information, negative sense content information and broadcast information weights.
The present invention also provides personalized recommendation system in a kind of intelligent video app, and it is included such as lower unit：
Monitoring thread dispensing unit, for configuring video app monitoring threads, and by the priority of video app monitoring threads
It is set to highest so that video app monitoring threads continuous service in mobile terminal；The video app monitoring threads are used to remember
The startup time of record video app, run time, end run time information；And when startup time according to video app, operation
Between, terminate run time information generation video app moving law charts；
Personal information acquiring unit, the personal information for obtaining user in video app；
Broadcast information acquiring unit, for when video is played out in video app, being regarded according to video tab information acquisition
The type information of frequency, and monitored by video app obtain pause in playing duration information, the playing process of the video, fall back, it is fast
Enter information；
Information extraction analytic unit, the circle of friends information of user is obtained for the personal information according to user in video app；
Personal information according to user predicts the preliminary video preference information of user；The aobvious of user is extracted from the circle of friends information of user
Property expressing information, analysis obtains the emotion preference of user from dominant expression information；
Recommend alternate list generation unit, regarded for the type information by the video in broadcast information acquiring unit
The Preliminary List of frequency personalized recommendation, by pause, retroversion, F.F. information pair in playing duration information, the playing process of video
The Preliminary List of video personalized recommendation be filtrated to get the alternate list of video personalized recommendation；
Consequently recommended list generation unit, for the emotion preference by user in information extraction analytic unit to video
The alternate list that propertyization is recommended screen and obtains final video personalized recommendation list；
List display unit, for judging whether video app starts, directly shows final on video app on startup
Video personalized recommendation list；When not actuated, the video app moving laws chart in monitoring thread dispensing unit is being moved
Final video personalized recommendation list is shown on dynamic terminal notification column.
In personalized recommendation system in intelligent video app of the present invention,
It also includes such as lower unit：
Recommend feedback unit, for obtain user for the video in final video personalized recommendation list click, broadcast
Record is put, and recorded click, played record, optimized for the video personalized recommendation list to next time.
In personalized recommendation system in intelligent video app of the present invention,
The personal information acquiring unit includes：
Obtain the privacy settings information of userspersonal information of the user on video app；Judged by privacy settings information
Whether there is the authority of the userspersonal information for obtaining user on video app；When with authority, user is obtained in video
Userspersonal information on app；The personal information includes dominant information, recessive information and the friendship network letter of user
Breath；The dominant information of the user includes age, sex, the occupational information of user；The recessive information of the user includes user
Video click information.
In personalized recommendation system in intelligent video app of the present invention,
Broadcast information weights are set in the broadcast information acquiring unit；Broadcast information weights are 1 plus during the broadcasting of video
Ratio with video duration long；
Monitor pause, the reverse information of user；When pause, the reverse information of user is got, extract pause when and
The first video frame information before and after play time after retroversion in the range of the scheduled time；
The content information of the first video frame information is obtained by graphical analysis, the content information of the first video frame information is made
It is positive content information；
Monitor the F.F. information of user；When the F.F. information of user is got, extract F.F. and start in termination interval
The second video frame information；
The content information of the second video frame information is obtained by graphical analysis, the content information of the second video frame information is made
It is negative sense content information；
And the positive content information of storage frame information, negative sense content information and broadcast information weights.
Personalized recommendation method and system in the intelligent video app that the present invention is provided, relative to prior art, can overcome
Being rested on the existing personalized recommendation method that prior art is present individual based on article, based on user or based on collaborative filtering more
Propertyization recommends method；It is not suitable for the defect of the recommendation of personalization in video app.
Brief description of the drawings
Fig. 1 be the embodiment of the present invention intelligent video app in personalized recommendation system structured flowchart.
Specific embodiment
The embodiment of the present invention provides personalized recommendation method in a kind of intelligent video app, and it comprises the following steps：
S0, configuration video app monitoring threads, and the priority of video app monitoring threads is set to highest so that video
App monitoring threads continuous service in mobile terminal；The video app monitoring threads be used for record video app the startup time,
Run time, end run time information；And startup time, run time, the information life of end run time according to video app
Into video app moving law charts；
By implementing this step, the video app that can persistently obtain user by video app monitoring threads activates operation shape
State, and form video app moving law charts.
S1, the personal information for obtaining user in video app；
S2, when video is played out in video app, according to the type information of video tab information acquisition video, and lead to
Cross video app and monitor pause, retroversion, F.F. information in the playing duration information, the playing process that obtain the video；
S3, the circle of friends information that user is obtained according to the personal information of user in video app；According to the personal information of user
Predict the preliminary video preference information of user；The dominant expression information of user is extracted from the circle of friends information of user, from dominant
Analysis obtains the emotion preference of user in expressing information；
S4, the Preliminary List that video personalized recommendation is obtained by the type information of the video in step S2, by video
Playing duration information, playing process in pause, fall back, F.F. information filters to the Preliminary List of video personalized recommendation
Obtain the alternate list of video personalized recommendation；
S5, the alternate list of video personalized recommendation is screened most by the emotion preference of user in step S3
Whole video personalized recommendation list；Final video personalized recommendation list is shown on video app；
S6, judge whether video app starts, directly show final video personalized recommendation on video app on startup
List；When not actuated, the video app moving laws chart in step S0 shows on mobile terminal informing and finally regards
Frequency personalized recommendation list.
By implementing this step, when video app is not actuated, the video app moving laws chart according to user carries out individual
Propertyization is recommended, it is to avoid the influence that the random push of app applications is caused to user in the prior art, improves user's body
Test.
In embodiments of the present invention, personal information, the broadcast information of video, the circle of friends information of user are combined, can
The preference of user is predicted from all angles, better than existing personalized recommendation algorithm.
In personalized recommendation method in intelligent video app of the present invention,
It also comprises the following steps：
The click of S7, acquisition user for the video in final video personalized recommendation list, broadcasting record, and measuring point
Hit, play record, optimized for the video personalized recommendation list to next time.
In personalized recommendation method in intelligent video app of the present invention,
The step S1 includes：
Obtain the privacy settings information of userspersonal information of the user on video app；Judged by privacy settings information
Whether there is the authority of the userspersonal information for obtaining user on video app；When with authority, user is obtained in video
Userspersonal information on app；The personal information includes dominant information, recessive information and the friendship network letter of user
Breath；The dominant information of the user includes age, sex, the occupational information of user；The recessive information of the user includes user
Video click information.
In personalized recommendation method in intelligent video app of the present invention,
Broadcast information weights are set in the step S2；Broadcast information weights are 1 plus when the playing duration of video and video
Ratio long；
Monitor pause, the reverse information of user；When pause, the reverse information of user is got, extract pause when and
The first video frame information before and after play time after retroversion in the range of the scheduled time；
The content information of the first video frame information is obtained by graphical analysis, the content information of the first video frame information is made
It is positive content information；
Monitor the F.F. information of user；When the F.F. information of user is got, extract F.F. and start in termination interval
The second video frame information；
The content information of the second video frame information is obtained by graphical analysis, the content information of the second video frame information is made
It is negative sense content information；
And the positive content information of storage frame information, negative sense content information and broadcast information weights.
By implementing the embodiment of the present invention, can in itself draw user for each fragment of the video from video information
Hobby so that follow-up recommendation is more accurate.
As shown in figure 1, the embodiment of the present invention also provides personalized recommendation system in a kind of intelligent video app, it is included such as
Lower unit：
Monitoring thread dispensing unit, for configuring video app monitoring threads, and by the priority of video app monitoring threads
It is set to highest so that video app monitoring threads continuous service in mobile terminal；The video app monitoring threads are used to remember
The startup time of record video app, run time, end run time information；And when startup time according to video app, operation
Between, terminate run time information generation video app moving law charts；
Personal information acquiring unit, the personal information for obtaining user in video app；
Broadcast information acquiring unit, for when video is played out in video app, being regarded according to video tab information acquisition
The type information of frequency, and monitored by video app obtain pause in playing duration information, the playing process of the video, fall back, it is fast
Enter information；
Information extraction analytic unit, the circle of friends information of user is obtained for the personal information according to user in video app；
Personal information according to user predicts the preliminary video preference information of user；The aobvious of user is extracted from the circle of friends information of user
Property expressing information, analysis obtains the emotion preference of user from dominant expression information；
Recommend alternate list generation unit, regarded for the type information by the video in broadcast information acquiring unit
The Preliminary List of frequency personalized recommendation, by pause, retroversion, F.F. information pair in playing duration information, the playing process of video
The Preliminary List of video personalized recommendation be filtrated to get the alternate list of video personalized recommendation；
Consequently recommended list generation unit, for the emotion preference by user in information extraction analytic unit to video
The alternate list that propertyization is recommended screen and obtains final video personalized recommendation list；
List display unit, for judging whether video app starts, directly shows final on video app on startup
Video personalized recommendation list；When not actuated, the video app moving laws chart in monitoring thread dispensing unit is being moved
Final video personalized recommendation list is shown on dynamic terminal notification column.
In personalized recommendation system in intelligent video app of the present invention,
It also includes such as lower unit：
Recommend feedback unit, for obtain user for the video in final video personalized recommendation list click, broadcast
Record is put, and recorded click, played record, optimized for the video personalized recommendation list to next time.
In personalized recommendation system in intelligent video app of the present invention,
The personal information acquiring unit includes：
Obtain the privacy settings information of userspersonal information of the user on video app；Judged by privacy settings information
Whether there is the authority of the userspersonal information for obtaining user on video app；When with authority, user is obtained in video
Userspersonal information on app；The personal information includes dominant information, recessive information and the friendship network letter of user
Breath；The dominant information of the user includes age, sex, the occupational information of user；The recessive information of the user includes user
Video click information.
In personalized recommendation system in intelligent video app of the present invention,
Broadcast information weights are set in the broadcast information acquiring unit；Broadcast information weights are 1 plus during the broadcasting of video
Ratio with video duration long；
Monitor pause, the reverse information of user；When pause, the reverse information of user is got, extract pause when and
The first video frame information before and after play time after retroversion in the range of the scheduled time；
The content information of the first video frame information is obtained by graphical analysis, the content information of the first video frame information is made
It is positive content information；
Monitor the F.F. information of user；When the F.F. information of user is got, extract F.F. and start in termination interval
The second video frame information；
The content information of the second video frame information is obtained by graphical analysis, the content information of the second video frame information is made
It is negative sense content information；
And the positive content information of storage frame information, negative sense content information and broadcast information weights.
Personalized recommendation method and system in the intelligent video app that the present invention is provided, relative to prior art, can overcome
Being rested on the existing personalized recommendation method that prior art is present individual based on article, based on user or based on collaborative filtering more
Propertyization recommends method；It is not suitable for the defect of the recommendation of personalization in video app.
The method that is described with reference to the embodiments described herein or algorithm can directly use hardware, computing device
Software module, or the two combination is implemented.Software module can be placed in random access memory, internal memory, read-only storage, electricity can
It is known in programming ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field
In the storage medium of any other forms.
It is understood that for the person of ordinary skill of the art, can be done with technology according to the present invention design
Go out other various corresponding changes and deformation, and all these changes and deformation should all belong to the protection model of the claims in the present invention
Enclose.
Claims (8)
1. personalized recommendation method in a kind of intelligent video app, it comprises the following steps：
S0, configuration video app monitoring threads, and the priority of video app monitoring threads is set to highest so that video app
Monitoring thread continuous service in mobile terminal；The video app monitoring threads are used to record the startup time of video app, fortune
Row time, end run time information；And startup time, run time, the information generation of end run time according to video app
Video app moving law charts；
S1, the personal information for obtaining user in video app；
S2, when video is played out in video app, according to the type information of video tab information acquisition video, and by regarding
Frequency app monitors pause, retroversion, F.F. information in the playing duration information, the playing process that obtain the video；
S3, the circle of friends information that user is obtained according to the personal information of user in video app；Personal information prediction according to user
The preliminary video preference information of user；The dominant expression information of user is extracted from the circle of friends information of user, from dominant expression
Analysis obtains the emotion preference of user in information；
S4, the Preliminary List that video personalized recommendation is obtained by the type information of the video in step S2, by broadcasting for video
Put pause in duration information, playing process, fall back, F.F. information is filtrated to get to the Preliminary List of video personalized recommendation
The alternate list of video personalized recommendation；
S5, the alternate list of video personalized recommendation is screened by the emotion preference of user in step S3 finally regarded
Frequency personalized recommendation list；
S6, judge whether video app starts, directly show final video personalized recommendation list on video app on startup；
When not actuated, the video app moving laws chart in step S0 shows final video on mobile terminal informing
Property recommendation list.
2. personalized recommendation method in intelligent video app as claimed in claim 1, it is characterised in that
It also comprises the following steps：
S7, obtain user for the video in final video personalized recommendation list click, play record, and record click,
Record is played, is optimized for the video personalized recommendation list to next time.
3. personalized recommendation method in intelligent video app as claimed in claim 2, it is characterised in that
The step S1 includes：
Obtain the privacy settings information of userspersonal information of the user on video app；Judged whether by privacy settings information
With the authority for obtaining userspersonal information of the user on video app；When with authority, user is on video app for acquisition
Userspersonal information；The personal information includes dominant information, recessive information and the friendship network information of user；Institute
Stating the dominant information of user includes age, sex, the occupational information of user；The recessive information of the user includes the video of user
Click information.
4. personalized recommendation method in intelligent video app as claimed in claim 3, it is characterised in that
Broadcast information weights are set in the step S2；Broadcast information weights are 1 plus the playing duration of video and video duration
Ratio；
Monitor pause, the reverse information of user；When pause, the reverse information of user is got, extract when suspending and retroversion
The first video frame information before and after play time afterwards in the range of the scheduled time；
The content information of the first video frame information is obtained by graphical analysis, using the content information of the first video frame information as just
To content information；
Monitor the F.F. information of user；When the F.F. information of user is got, extract F.F. start to terminate in interval the
Two video frame informations；
The content information of the second video frame information is obtained by graphical analysis, using the content information of the second video frame information as negative
To content information；
And the positive content information of storage frame information, negative sense content information and broadcast information weights.
5. personalized recommendation system in a kind of intelligent video app, it is included such as lower unit：
Monitoring thread dispensing unit, for configuring video app monitoring threads, and the priority of video app monitoring threads is set
It is highest so that video app monitoring threads continuous service in mobile terminal；The video app monitoring threads are regarded for record
The startup time of frequency app, run time, end run time information；And according to startup time, run time, the knot of video app
Beam run time information generates video app moving law charts；
Personal information acquiring unit, the personal information for obtaining user in video app；
Broadcast information acquiring unit, for when video is played out in video app, according to video tab information acquisition video
Type information, and pause, retroversion, F.F. letter in the playing duration information, the playing process that obtain the video are monitored by video app
Breath；
Information extraction analytic unit, the circle of friends information of user is obtained for the personal information according to user in video app；According to
The personal information of user predicts the preliminary video preference information of user；The dominant table of user is extracted from the circle of friends information of user
Up to information, analysis obtains the emotion preference of user from dominant expression information；
Recommend alternate list generation unit, video is obtained for the type information by the video in broadcast information acquiring unit
The Preliminary List that propertyization is recommended, by pause, retroversion, F.F. information in playing duration information, the playing process of video to video
The Preliminary List of personalized recommendation be filtrated to get the alternate list of video personalized recommendation；
Consequently recommended list generation unit, it is personalized to video for the emotion preference by user in information extraction analytic unit
The alternate list of recommendation screen and obtains final video personalized recommendation list；
List display unit, for judging whether video app starts, directly shows final video on video app on startup
Personalized recommendation list；When not actuated, the video app moving laws chart in monitoring thread dispensing unit is mobile whole
Final video personalized recommendation list is shown on the informing of end.
6. personalized recommendation system in intelligent video app as claimed in claim 5, it is characterised in that
It also includes such as lower unit：
Recommend feedback unit, for obtaining click of the user for the video in final video personalized recommendation list, broadcasting note
Record, and record click, play record, optimized for the video personalized recommendation list to next time.
7. personalized recommendation system in intelligent video app as claimed in claim 6, it is characterised in that
The personal information acquiring unit includes：
Obtain the privacy settings information of userspersonal information of the user on video app；Judged whether by privacy settings information
With the authority for obtaining userspersonal information of the user on video app；When with authority, user is on video app for acquisition
Userspersonal information；The personal information includes dominant information, recessive information and the friendship network information of user；Institute
Stating the dominant information of user includes age, sex, the occupational information of user；The recessive information of the user includes the video of user
Click information.
8. personalized recommendation system in intelligent video app as claimed in claim 7, it is characterised in that
Broadcast information weights are set in the broadcast information acquiring unit；Broadcast information weights be 1 plus the playing duration of video with
The ratio of video duration；
Monitor pause, the reverse information of user；When pause, the reverse information of user is got, extract when suspending and retroversion
The first video frame information before and after play time afterwards in the range of the scheduled time；
The content information of the first video frame information is obtained by graphical analysis, using the content information of the first video frame information as just
To content information；
Monitor the F.F. information of user；When the F.F. information of user is got, extract F.F. start to terminate in interval the
Two video frame informations；
The content information of the second video frame information is obtained by graphical analysis, using the content information of the second video frame information as negative
To content information；
And the positive content information of storage frame information, negative sense content information and broadcast information weights.
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