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Collaborative personalized tweet recommendation
- 1. Collaborative Personalized Tweet Recommendation Kailong Chen, Tianqi Chen, Guoqing Zheng, Ou Jin, Enpeng Yao, Yong Yu Shanghai Jiao Tong Univ. To Appear in SIGIR 2012 WUME Reading Group Liangjie Hong
- 2. Outline • Problem • Contribution & Assumptions • Model • Dataset & Experiments
- 3. The Problem
- 4. Contributions • Topic latent factors to capture users’ interests • Social latent factors to model social relations • Incorporate Explicit features • Build on traditional collaborative ranking approach
- 5. Assumptions • Users’ retweeting actions reflect their personal judgement of informativeness and usefulness. • Users who have retweeted similar statuses in the past are likely to retweet similar statuses in the future.
- 6. Starting from scratch To predict the response, the simplest MF model: bias latent factors
- 7. Starting from scratch Learning pair-wise preferences
- 8. Starting from scratch Objective function
- 11. Incorporating Features Combine Topic + Social
- 14. Incorporating Features Features • Relation features ▫ Co-follow scores ▫ Mention scores # of times user u has mentioned publisher p ▫ Friend binary indicator u <-> p
- 15. Incorporating Features Features • Relevance features ▫ Relevance to status history ▫ Relevance to retweet history ▫ Relevance to hash tags
- 16. Incorporating Features Features • Content-meta features ▫ Length of tweet ▫ Hash tag count ▫ URL count ▫ Retweet count
- 17. Incorporating Features Features • Publishers’ Authority features ▫ Mention count ▫ Followee count ▫ Follower count ▫ Status count
- 18. Parameter Estimation • Stochastic gradient descent • Down-sample negative samples
- 19. Dataset • 8059 users in the base with all statuses ▫ Select 1048 users: > 15 followees ▫ one (+) vs. four (-) ▫ 490 tweets on average per user ▫ 409680 (4/5) in training ▫ 102457 (1/5) for testing
- 20. Dataset
- 21. Evaluation metrics • Redefined MAP
- 22. Method Comparison • Chronological • Retweeted Times • Profiling • LDA • RankSVM • JointMF ▫ [Yang et al, WWW 2011] • CTR ▫ This paper.
- 23. Results
- 24. Results
- 25. Results
- 26. Conclusion • Model: a modified feature based MF model • Novelty ▫ The task ▫ Topic decomposition ▫ Feature integration