WWW 2009

1. Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models Wei Chu Seung-Taek Park Audience Science Yahoo! Labs. WWW 2009

2. Outline • Dynamic content • Yahoo! Front Page Today Module • Difficulties on new users and new items • Personalized recommendation • Global level, “one-size-fits-all” / “most popular” • Segmented level, “segmentation” • Individual level, “personalization” • Methodology • Predictive bilinear models • Findings in the case study • Conclusions WWW 2009

3. Dynamic Content Yahoo! Front Page WWW 2009

4. Dynamic Content Today Module • At default, the article at F1 is highlighted at the Story position. • Articles are selected from a hourly-refreshed article pool. • Replacement on out-of-date articles happens every a few hours. • GOAL: select the most attractive article for the Story position to draw users’ attention and then increase users’ retention. WWW 2009

5. Dynamic Content Today Module • Click-through rate (CTR) is decaying temporally, e.g. breaking news. • About 40% clickers are first-time clickers. • Lifetime of an article is usually short, only a few hours; • The universe of content pool is dynamic. 9 days’ data WWW 2009

6. Difficulties on Dynamic Content • Collaborative filtering provides good solution to “a closed world” • Overlaps in feedback across users are relatively high • The universe of content items is almost static • CTR is decaying temporally • Difficult to compare users’ feedback on the same article received at different time slots • Lifetime of an article is usually short, only a few hours • Reduce overlaps in feedback across users • The universe of content pool is dynamic • Have to wait for clicks on new items (content-based filtering helps) • Storage and retrieval of historical ratings of retired items are demanding • About 40% clickers are first-time clickers • Hard on new users without historical ratings (“most popular” is baseline) Cold-Start Recommendation WWW 2009

7. Solution: Feature-based modeling • Users with open profiles • Demographical information, age, gender, location • Property usage over Yahoo! networks • Search logs, purchase history etc. • Content profile management • Static descriptors: categories, title, bags of words of textual content etc. • Temporal characteristics: popularity, CTR, freshness etc. • Feature-based regression models for personalization at individual level • New items: initialize popularity based on static meta features • New users: estimate preferences on items based on user features WWW 2009

8. Methodology C100 • Data representation • User features (D features per user) • Content features (C features per article) • Historical feedback (“story click” or not) • Predictive bilinear models • Bilinear score for a user/article pair • the b-th feature of user • the a-th feature of item • affinity between and D1000 1.5 0.7 1.1 1.3 WWW 2009

9. Offline Optimization C100 • Model fitting on historical feedback • Regression on continuous targets • Logistic regression on binary targets • Probabilistic framework • Optimal affinities at maximum a posteriori (MAP) estimate • Prediction D1000 1.5 0.7 WWW 2009

10. Case Study • Data collection • Random serving policy • Temporal partition • About 40 million events for training • About 5 million distinct users • Test events (about 0.6 million “story click”s) • Offline performance metric • “Click Portion”: the fraction of clicks at rank position r Application: Front Page Today Module WWW 2009

11. Case Study • Data collection • Random serving policy • Temporal partition • About 40 million events for training • About 5 million distinct users • Test events (about 0.6 million “story click”s) • Offline performance metric • “Click Portion”: the fraction of clicks at rank position r Application: Front Page Today Module Click Rank : 2 at the moment of the click event in test Like Dislike WWW 2009

12. Case Study • Data collection • Random serving policy • Temporal partition • About 40 million events for training • About 5 million distinct users • Test events (about 0.6 million “story click”s) • Offline performance metric • “Click Portion”: the fraction of test clicks at rank r Application: Front Page Today Module Click Rank : 1 at the moment of click events in test Like Dislike WWW 2009

13. Case Study • Baseline: select the article with the highest CTR (EMP) • “One-size-fits-all” approach by online CTR tracking (Agarwal et al. NIPS 2009; Agarwal et al. WWW 2009) • Segmentation • Age/gender-based segmentation with 6 clusters (GM) • Conjoint analysis with 5 clusters (Chu et al. KDD 2009) (SEG5) • Collaborative filtering • Item-based collaborative filtering (IBCF) • Content-based filtering (CB) • Hybrid CB with CTR (CB+EMP) : • Feature-based personalized models • Bilinear regression (RG) • Logistic bilinear regression (LRG) • LRG without article CTR feature (LRG-CTR) Matchbox: Large Scale Bayesian Recommendations Stern, Herbrich and Graepel (WWW2009) Microsoft Res. Thursday XL-2, Statistical Methods WWW 2009

14. Case Study • Lift over the baselineEMP “one-size-fits-all” • SEG5: tensor conjoint analysis with 5 clusters • CB+EMP: • Logistic Bilinear Models WWW 2009

15. Case Study • A utility function (overall performance at top 4 positions) • where is “Click Portion” at rank r WWW 2009

16. Summary • Feature-based bilinear regression models for personalized recommendation on cold-start situation of dynamic content. • The affinities between user attributes and content features are optimized by learning from historical user feedback. • Alleviate cold-start difficulties by leveraging available information at both user and item sides. • Significantly outperform six competitive approaches at global, segmented or individual levels on an offline metric. WWW 2009

17. Acknowledgment • We thank our colleague: • Raghu Ramakrishnan • Scott Roy • Deepak Agarwal • Bee-Chung Chen • Pradheep Elango • Ajoy Sojan • Todd Beaupre • Nitin Motgi • Amit Phadke • Seinjuti Chakraborty • Joe Zachariah WWW 2009

18. Questions?