Mining Cross-network Association for YouTube Video Promotion

1. Mining Cross-network Association for YouTube Video Promotion Ming Yan Institute of Automation, Chinese Academy of Sciences May 15, 2014

2. Outline • Motivation • Three-stageFramework • Some Visualization • Further Discussion

3. Background • Large quantities of videos are consumed in YouTube and the trend is growing year by year. • More than 1 billion unique users visit YouTube each month. • Over 6 billion hours of video are watched each month on YouTube. • 100 hours of video are uploaded to YouTube every minute. • YouTube exhibits limited propagation efficiency and many videos remain unknown to the wide public. • Long tail effect for the video view count distribution. • Short active life span for most videos.

4. Background • YouTube video popularity limited by its internal mechanism. • Internal search • Related video recommendation • Channel subscription • Front page highlight • External referrers such as social media websites arise to be important sources to lead users to YouTube videos. • Twitter has been quickly growing as the top referrer source for web video discovery.

5. YouTube video Motivation • For specific YouTube video, to identify proper Twitter followees with goal to maximize video dissemination to the followers. Twitter followee watch Got 1 billion views in 5 months Twitter follower

6. Challenge • The heterogeneous knowledge association between YouTube video and Twitter followee • user-perceived • How to define the “properness” of candidate Twitter followee for a specific YouTube video • interestness • virtual cost Our Twitter followee identification scheme actually expects to find the optimal Twitter followee whose followers are more likely to show interest to the target video.

7. User-perceived Solution • Illustration example better promotion referrer follow follow User Association favor view view

8. Framework • Three Stages

9. Heterogeneous Topic Modeling Twitter users ACM Multimedia 2014 @acmmm14 NBA @NBA • Input • YouTube video : [] • Twitter users with their follower set • Output • Twitter user distribution • YouTube user distribution Following Bill Gates @BillGates Britney Spears @britneyspears LDA Username @TwitterID … Twitter user distribution … • Topic Modeling Approach • On YouTube Side: • Propose an inverse Corr-LDA model to discover the YouTube video multimodal topics. YouTube video distribution … • On Twitter Side: • Standard LDA on Twitter followee-follower social graph. • user as document • user’s followees as word iCorr-LDA YouTube videos

10. Cross-network Topic Association overlapped users • Input • Twitter user and video distribution • and (output of stage 1) • YouTube, Twitter and the overlapped user set • YouTube user interested video set • Output • Distribution transfer function • (: the aggregated YouTube user distribution) Association Mining Interested videos Aggregation … username YouTube user distribution • Approach • YouTube User Aggregation • Association Mining

11. Cross-network Topic Association • YouTube User Aggregation user ’s interested videos … : the total number of keyframes and words in video : the total number of keyframes and words in ’s video set

12. Cross-network Topic Association • Association Mining • Goal: • To obtain the association between the YouTube video space and Twitter user space. (i.e. ) • Approach: • Transition Probability-based Association • Regression-based Association • Latent Attribute-based Association overlapped users Explicit association/transition matrix: Association Mining

13. Cross-network Topic Association • Transition Probability-based Association • Regression-based Association The overlapped users’ distribution matrix in Twitter and YouTube q=1: lasso problem and can be effectively solved by LARS and feature sign algorithm q=2: ridge regression problem and with analytical solution as

14. Cross-network Topic Association • Latent Attribute-based Association (non-linear) • only on overlapped users • on all users • Innovation: To discover shared latent structure behind the two topic spaces. (After projected to the latent attribute spaces, user’s YouTube and Twitter distribution share the same coefficient.) shared latent user attribute • Only on overlapped users By some simple transfer, it can be efficiently solved by the sparse coding algorithm.

15. Cross-network Topic Association • Latent attribute discovery on all users (plenty of non-overlapped users are considered in this scheme) • Objective function • Iteratively solved via three sub-problems

16. Referrer Identification test YouTube video • Input • Distribution transfer function • Test videos • Twitter followee set Distribution Transfer • Output • Twitter followee rank for each video Matching • Approach • Direct product-based matching • Weighted product-based matching … candidate Twitter followees

17. Referrer Identification • Direct product-based matching • Weighted product-based matching • Ranking SVM algorithm is used to train the weights: • Feature: • Training label: a designed properness score • With the learnt model parameter In charge of the coverage of the interested audiences In charge of the virtual cost

18. Some Visualization

19. Further Discussion • Some Extensible Application • Examining the value of Twitter followees(Our work can be viewed as valuing Twitter followee w.r.t. promotion efficiency to YouTube videos) (e.g. the followee has a lot of young female followers) • Advertising (Advertising media selection for our work) (e.g. anchor text generation (i.e., optimizing video description for promotion), advertising slot bid (i.e., followeereshare time selection))

20. Other user-bridged cross network application Challenge Data hard to get! Taobao Topic Tweet Topic 1 user recommend Advertisement Video 2