Shanda Innovations

1. Context-aware Ensemble of Multifaceted Factorization Models for Recommendation Kevin Y. W. Chen Shanda Innovations

2. Performance • 0.43959(public score)/0.41874(private score) • 2ndplace  Honorable Mention

3. New Challenges • Richer features in the social networks • follower/followee, actions • Items are complicate • items are specific users • Cold-start problem • 77.1% users do not have training records • Training data is quite noisy • ratio of negative samples is 92.82%

4. Outline • Preprocessing • denoise • supplement • Pairwise Training • Max-margin optimization problem • Multifaceted Factorization Models • Extend the SVD++ • Context-aware Ensemble • Logistic Regression

5. Preprocess: Session analysis • Negative : Positive = 92 : 8 ? • not all the negative ratings imply that the users rejected to follow the recommended items • Eliminating these “omitted” records is necessary • These negative samples can not indicate users' interests

6. Preprocess: Session analysis • Session slicing according to the time interval • Select the right samples from the right session:

7. Preprocess: Session analysis • Training dataset after preprocessing • Negative: 67,955,449 -> 7,594,443 (11.2%) • Positive: 5,253,828 ->4,999,118 • Benefits • improve precision (0.0037) • reduce computational complexity

8. Pairwise-training • MAP • pairwise ranking job • Training pair • (u, i) and (u, j) • Objective function

9. Preprocess: Supply positive samples • Lack of positive samples • An ideal pairwise training requires a good balance between the number of negative and positive samples • Choose the users • users who have a far smaller number of positive samples than negative samples • Generate the positive samples • Figure out from social graphs

10. The procedure of data preprocessing

11. Multifaceted Factorization Models • Latent Factor Model • stochastic gradient descent • MFM extends the SVD++ • integrate all kinds of valuable features in social networks

12. MFM: Demographic features • User and item profiles • age(u), age(i) • gender(u), gender(i) • tweetnum(u) • Combinations • uid*gender(i) • uid*age(i) • gender(u)*iid • age(u)*iid

13. MFM: Integrate Social Relationships • Influence of social relations • Cold start: • 77.1% users do not have any rating records in the training set • User feature vector: • Incorporate SNS relations and actions • Bring significant improvement • MAP: 0.3495 ->0.3688 ->0.3701

14. MFM: Utilizing Keywords and Tags • Share common interests • explicit feedbacks • User feature vectors:

15. MFM: Date-Time Dependent Biases • Users' action differs when time changes • The popularities of items change over time

16. k-Nearest Neighbors • Similar to SVD++ • Find the neighbors • calculate the distance based on Keywords and tags • Intersection of explicit and implicit feedbacks

17. Ensemble • When will the user follow an item? • pay attention to the item • be interested in the item • User behavior modeling • predict whether the user noticed the recommendation area at that time • User interest modeling • a item meet the user’s tastes -- MFM

18. User Behavior Modeling • Durations of users on each recommendation are very valuable clues • Context of durations

19. Experiment

20. Experiment

21. The framework

22. Summary • A proper data preprocessing is necessary • Pairwise training (top-N recommendation) • Social relations and actions can be used as implicit feedbacks • Integrate all kinds of valuable features • Users' interests and users' behaviors are both need to be considered

23. Shanda Innovations Team • Yunwen Chen, Zuotao Liu, DaqiJi, YingweiXin, Wenguang Wang, Lu Yao, Yi Zou

24. THANK YOU ! Kevin Y. W. Chen Shanda Innovations kddchen@gmail.com

25. Q&A Kevin Y. W. Chen Shanda Innovations kddchen@gmail.com