PRemiSE : Personalized News Recommendation via Implicit Social Experts

1. PRemiSE:Personalized News Recommendation via Implicit Social Experts

2. Overview • Introduction • Expert model • PRemiSE • Experimental • Future work

3. Google News

4. Existing news recommender systems • Content-based Recommenders bag-of-word model : document word topic models : document topic word • Collaborative Filtering KNN MF PMF • Hybrid Recommenders combining social network

5. Two problems in previous studies • data sparsity • cold-start problem PRemiSE：incorporating content information, collaborative filtering and information diffusion in virtual social network into probabilistic matrix factorization.

6. Our contribution • Capable of handling the cold-start problem • Semantically interpretable • Producing better predictions

7. Expert Model

8. An illustrative example of implicit social network

9. Building Implicit Social Network Step0：Compute time span & number of visits for each item Step1：Plot the time span ，number of visits ，find the abnormal items ，remove it Step2：Build the graph，based on user-item accessing history if U1 access the same item V after U2，and access_time(U1) – access_time(U2) < time_window , we say in the graph , there is an directed edges from U2 to U1. Step3 : Normalized weights Time_window: find enough neighbors for each user precisely find the right experts

10. Empirical study on a real data set

11. Local Expert and Global Expert • How probably the given user will follow the expert’s adoption on the same item? • How probably any individual will follow the expert’s adoption on the same item? find global expert？

12. PRemiSE • Matrix factorization • Probabilistic matrix factorization • PRemiSE • Learning in PRemiSE • Inference in PRemiSE

13. Matrix factorization item i user u = Now how to get Gradient descent

14. Probabilistic matrix factorization Linear probability model

15. PRemiSE

16. Learning in PRemiSE

17. Optimization Algorithm • See detailed in the paper

18. Inference in PRemiSE • Existing Item by Existing User • Existing Item by New User • New Item by Existing User • New Item by New User

19. Experimental Evaluation • Real-World Dataset 1. crawled from several popular news service websites 2. two types of elements : news stories and named entities. • Rating 1. Rating in Story：binary 2. Rating in Entity：numerical

20. Construction of Networks Step1：eliminate outlier items employing by ELKI Step2: The size of time-window set to be 8 days. we delete edges that are caused by a delayed co-consumption (9 days or even longer) step 3, we normalize the edges weight, and empirically set the edge weight threshold as 0.001

21. Parameters of Global Expert Model

22. Comparative Study

23. Cold-start problem

24. Semantics of factors

25. Conclusion AND Future work • We integrate this “expert” model with the content information and collaborative filtering, and propose a hybrid recommendation framework, called PRemiSE. • effectively handle the cold-start problem • better Semantics Explanation • better performance in recommendation accuracy • FUTURE WORK : social media & information diffusion model & export model

26. Thank you for your time!