Unified YouTube Video Recommendations: Cross-network Collaboration

1. Unified YouTube Video Recommendation via Cross-network Collaboration Ming Yan, JitaoSang, ChangshengXu Institute of Automation, Chinese Academy of Sciences Chinese-Singapore Institute of Digital Media • June 25, 2015

2. Unified Video Recommendation new user newly register with empty history view count Cold-start & Sparsity: inadequate user data in the target network Recommender Engine light user heavy user limited behavior records frequent interaction

3. Cross-network User Participation Can we exploit the user data from auxiliary networks for enhanced user modeling? • People usually involve in various social media networks simultaneously

4. Cross-network Collaboration new user empty history ? initial recommendation for warm up more fine-grained recommendation new technology lover Recommender Engine James fan tweets Mike @ michael Cheer up! @KingJames! Mike @ michael basketball Overtime. LeBron has 42 points. light user … … follow

5. Cross-network Collaboration game sport follow diverse & novel recommendation To design a unified video recommendation solution which can facilitate all the three types of users, by exploiting the cross-network user data. politics follow Recommender Engine heavy user Motivation:

6. Data Justification • Question 1: • Is it easy to obtain user accounts across different social media networks? • Question 2: • Are users’ Twitter data adequate to make up the shortage on YouTube video- • related user data?

7. Data Justification • Question 1: • Is it easy to obtain user accounts across different social media networks? • Observation 1: User are voluntary to disclose their multiple-OSN accounts. Noticeable (> 30%) #user: Figure 1: Proportion of users who share accounts in other OSNs within the total 137,317 Google+ users overlapped users

8. Data Justification • Question 1: • Is it easy to obtain user accounts across different social media networks? • Observation 1: User are voluntary to disclose their multiple-OSN accounts. This opens up opportunities for large-scale cross-network collaboration practices. #user: Huge overlap (> 50%) Figure 2: User overlap ratio between four OSNs overlapped users

9. Data Justification This validates our motivation to address YouTube cold-start and spasity by leveraging Twitter data. • Observation 2:User’s Twitter tweeting behaviors are adequate to make up the shortage in video interactions on YouTube. 17,617 users The red-color points locate largely in the upper left of the diagonal line. Although sparse on YouTube, but with adequate tweeting behaviors on Twitter. the number of users having the corresponding behavior count combination #overlapped users: API API recent 1,000 tweets uploads, favorite and playlist Figure 3: The heatmap of user behavior counts on YouTube and Twitter

10. Challenges • Inconsistency between the auxiliary and target network favorite • Cross-network knowledge gap

11. Solution: Framework cross-network user data user tweets videos Knowledge gap 1 Auxiliary-network Data Transfer Inconsistency 2 Cross-network Data Integration video preference matrix newuser lightuser … heavyuser

12. Solution: Input cross-network user data Twitter tweet behaviors YouTube user-video interactions Marry @marry123 LeBron has 42 points. newuser … … user tweets videos lightuser Mike @michael … Cheer up! @KingJames! 1 heavyuser Auxiliary-network Data Transfer 2 Cross-network Data Integration video preference matrix newuser lightuser … heavyuser

13. Solution: Auxiliary-network Data Transfer cross-network user data Twitter tweet behaviors Marry @marry123 LeBron has 42 points. … … user tweets videos Mike @michael Cheer up! @KingJames! 1 Auxiliary-network Data Transfer 2 Cross-network Data Integration video preference matrix newuser Technology Game Sports √ √ ╳ ╳ lightuser … … ╳ ╳ √ heavyuser transferred YouTube user model

14. Solution: Auxiliary-network Data Transfer Twitter tweet behaviors Twitter tweet topic distri. Marry @marry123 Input topic modeling Twitter tweet topic space LeBron has 42 points. … … … Mike @michael Cheer up! @KingJames! YouTube latent space Knowledge gap Technology Game Sports learn the cross-network transfer matrix W. √ √ ╳ ╳ Output … ╳ ╳ √ video preference transferred YouTube user model

15. Solution: Auxiliary-network Data Transfer Supervised Training: Learn with the observations of users’ Twitter distribution and YouTube video interaction matrix Content-based Laplacian regularization Transfer-based rating matrix factorization stochastic gradient descent new user Twtter tweet distri. YouTube video preference

16. Solution: Cross-network Data Integration cross-network user data observed YouTube user-video interactions R transferred YouTube user model Game Sports Technology Game Sports Technology ╳ √ √ √ ╳ ╳ user tweets videos √ ╳ ╳ … … … light and heavy users updated YouTube user model 1 Auxiliary-network Data Transfer 2 Cross-network Data Integration YouTube latent space video preference matrix √ newuser √ √ √ lightuser … ╳ heavyuser Data Integration

17. Solution: Cross-network Data Integration transferred YouTube user model Game Sports Technology Game Sports Technology ╳ √ √ √ ╳ Input ╳ √ ╳ ╳ … … Game Game updated YouTube user model YouTube latent space Inconsistency need to balance the contribution of and R Sports Game Tech. √ : video latent representation √ √ √ Output ╳ video preference

18. Solution: Cross-network Data Integration User Model Update: Update user model with the observed YouTube interactions by viewing the transferred user model as the prior Twitter transferred user model as prior (from stage 1) YouTube interaction factorization … video re-alignment user-specific weighting matrix

19. Solution: Cross-network Data Integration User Model Update: Update user model with the observed YouTube interactions by viewing the transferred user model as the prior light user heavy user

20. Challenging: finding 10~20 ground-truth videos from a 4400+ video collection. Experiment: Performance Evaluation • Evaluation dataset • Keep the users who interacted with > 10 YouTube videos and posted > 10 tweets • Videos interacted by < 3 users are also filtered out • 2,560 overlapped users and 4,414 YouTube videos • Sparsity: 99.45% Training Set Test Set Figure 4: The boxplot statistics of users’ video-related behaviors on YouTube for the three types of users

21. Experiment: Performance Evaluation • Baselines • Popularity: recommend according to the video view count • KNN: Item-based KNN • LFM: Latent Factor Model • rPMF: Probabilistic MF method with video content Laplacian regularization • Proposed approach • auxTransfer: only considers stage 1 • crossIntegration: combines both stage 1 and stage 2 • Evaluation metrics • Top-k precision, recall and F-score New User Light User Heavy User single network-based cross network-based Figure 5: Performance evaluation on F-score

22. Experiment: Performance Evaluation • Visualization of the obtained transfer matrix W Visualization of discovered tweet topics Game 50 Twitter tweet topic The top-3 videos in terms of video latent representation (video latent factor distri.) Topic #7 Epic Mods -MW2 in CoD4 Minecraft Song: “I Hate Creepers”… Angry Birds Star Wars… YouTube latent topic 7 Visualization of YouTube latent topics

23. Experiment: Performance Evaluation • Visualization of the obtained transfer matrix W beer Visualization of discovered tweet topics 40 The top-3 videos in terms of video latent representation Twitter tweet topic (video latent factor distri.) Topic #15 Obama Blasts Romney on… Will Ron Paul Endorse Mitt… Obama Tax Cuts… 15 politics YouTube latent topic Visualization of YouTube latent topics

24. Experiment: Discussion • Limited cross-network data V.S. adequate single-network data > VS + adequate limited • Will cross+limited beat single+adequate? • Other advantages except for accuracy? tweets Mike @ michael Single-network solution Cheer up! @KingJames! Mike @ michael Overtime. LeBron has 42 points. … … Cross-network solution

25. Experiment: Discussion • Limit cross Vs adequate single Recommender systems must provide not just accuracy, but also usefulness. --JONATHAN L. HERLOCKER Exploiting cross-network user data contributes to understanding users’ distributed interests towards serendipity recommendation. intra-list similarity by video content single network-based cross network-based taking both the video popularity and user behavior sparsity into consideration

26. Conclusion • [Significant Problem]: We propose a unified video recommendation framework, which simultaneously addresses three longstanding problems in recommender systems: new user, cold-start and sparsity; • [Novel Solution]: We introduce a novel personalized recommendation solution by leveraging cross-network user data; • [Comprehensive evaluation]: The proposed solution obtains superior performance on three kinds of typical users, in term of not only accuracy, but also diversity and novelty.

27. Prospect Google+ + YouTube Facebook + Yelp … Twitter + Amazon Weibo + Taobao 2016 2008 2010 2012 2014 Year Two stages of cross-network cooperation: cross-network user sharing cross-networkuser modeling

28. Backup slides

29. Backup slides

30. Backup slides • Dataset Partition • 1,060 active users for training W at the first stage (>30 video-related behaviors and > 200 tweets) • The remaining 1,500 users are evenly separated into three subsets according to the number of video-related interaction: • : 30% for training, 70% for evaluation • : 80% for training, 20% for evaluation

31. Backup slides Google+ + YouTube Facebook + Yelp connect Twitter + Amazon Weibo + Taobao • The trend of cross-network cooperation: user sharing

32. Backup slides • Visualization of the obtained transfer matrix for social links Berlin popular users Visualization of discovered social topics Twitter social topic The top-3 videos in terms of video latent representation (video latent factor distri.) Topic #14 YouTube latent topic GEH STERBEN, DU OPFER… Syrien – Wahrheitber das Massaker… Volker Pispers - Einzeltater… German TV show Visualization of YouTube latent topics

33. Backup slides • Visualization of the obtained transfer matrix for social links Famous actor Visualization of discovered social topics Twitter social topic The top-3 videos in terms of video latent representation (video latent factor distri.) Topic #10 YouTube latent topic Assad Running Out of Time… Airsoft War L96 SNIPER… Why the US has no moral authority … War & politics Visualization of YouTube latent topics

34. Backup slides • The selection of other auxiliary-network information • social links, etc.