Personalized Paper Recommendation Based on User Historical Behavior

1. Personalized Paper Recommendation Based on User Historical Behavior Yuan Wang College of Information Technology Science, Nankai University, Tianjin, China kdd.nankai.edu.cn/wangy Major contributors:Jie Liu, XingLiang Dong, Tianbi Liu, and YaLou Huang Nankai University IIP Lab

2. Outline • Why need we provide personalized paper recommendation? • background & our motivation • Related approaches • Personalized Paper Recommendation model • Overview • Estimation for each part • The optimization of the model • Results • Data Collection • Evaluation • Conclusion

3. Background • the rapid development of Digital Libraries（DL） • for information sharing and search • more new idea first posted on DL • information overload and infromation lost： So many book， so little time~

4. Motivation • effective technique • How to tackle the problem now？ • sending a email • through RSS subscription • Users’ historical reveal users’ mind(*^__^*) • browsing • behaviors on the site( publishing, marking a paper as favorite, rating, making a comment, and tagging ) We need recommendation require user interests explicitly

5. Motivation • Focus on providing more relevance papers to researchers • Learn their personalized preference from their historical behaviors We need recommendation

6. Related approaches • Personalized Recommendation • Collaborative filtering • users always prefer things their friend • PHOAKS and REFERRAL Web, CiteSeer Search Amazon，ebay ， Douban， • mainly in Commercial recommendation system • Drawbacks • Cold Start：Pseudo Users add new score、clustering • Content-based filtering • based on content and user similarity • Web Watcher，LIRA，Leticia

7. Related approaches • Personalized recommendation for scientific papers • Collaborative filtering • Methods • citation relationship, similar to PageRank, less user preference • Learn from a recommendation system, then filtering, Input difficult to get • get preference from log, more noise • Drawbacks • content information should be taken consideration. • Content-based filtering • take care for messages carried with papers • without cold start and data sparse problem • statistical models are effective for paper recommendation

8. Outline • Why need we provide personalized paper recommendation? • background & our motivation • Related approaches • Personalized Paper Recommendation model • Overview • Estimation for each part • The optimization of the model • Results • Data Collection • Evaluation • Conclusion

9. Personalized Paper Recommendation model • A triple relationship for PPR • Description • Di : the document set to be recommended to the user. • Dx : the document set the user is viewing • U :the current user • Similarity between recommended resource and users (Di, Dx, U)

10. Personalized Paper Recommendation model •  Assumption • users and documents draw from i.i.d • Description • p(di) : Priori Probability of Paper • p(dx|di) : Similarity between Papers • p(uk|di):Similarity between User and Paper

11. Priori Probability of Paper • evaluate the probability a document will be selected • through global website by users’ historical behaviors • more valuable, more operation • A = {down, keep, visit, tag, score, comment, collect, ……} • absolute discount smoothing

12. Similarity between Papers • title, abstract, keywords and domain of area as documents’ feature • calculate the similarity through word segmentation • w : each word in a document • tf (w; di) : the frequency in which w appears in di • tf (di) : the frequency of all words in di. • tf (w, D) : the frequency that w appears in all documents • tf (D) : the total frequency all words appear in all documents • a : a parameter used for smoothing ( 0.1 here)

13. Similarity between User and Paper • Actions on papers reveal users’ preference • words in users and documents draw from i.i.d • Wk : the set of each word in user’s profile • tf (w; uk) : the frequency in which w appears in profile of uk • P(w|di) : calculate as similarity between papers

14. The optimization of the model • Original model did’t consider users’ preference transitive relation • Optimize model by Matrix Transition User A User B User C

15. The optimization of the model Random Walk on graph normalize by colume normalize by row

16. The optimization of the model Random Walk on graph normalize by colume normalize by row

17. Outline • Why need we provide personalized paper recommendation? • background & our motivation • Related approaches • Personalized Paper Recommendation model • Overview • Estimation for each part • The optimization of the model • Results • Data Collection • Evaluation • Conclusion

18. Data Collection • Train Data • Website: www.paper.edu.cn • User behaviors: publish, keep, download, visit, tag, score and make comments about papers • Paper：first publish of papers from October 1, 2010 to March 1, 2011. • Test Data • (U, Dx, Di, L) • 638 data samples: 339 labeled with 1 and 299 with 0. • 26 users and 93 papers

19. Evaluation Matrix • MAP • Mean Average Precision • show us the accuracy of models • NDCG • Normalized Discounted Cumulative Gain • list accuracy evaluation based • NDCG@1 to NDCG@6.

20. Results • Personalized or not • five percent increase • the average improvement of NDCG is 10.2%.

21. Results • User preference iteration • The more iterations, the sharper MAP declines • the original information is lost as the number of iterations increases

22. Results • optimization of model • “ori” means original model, “n” means the iteration times. • ori+1 get the highest score.

23. Outline • Why need we provide personalized paper recommendation? • background & our motivation • Related approaches • Personalized Paper Recommendation model • Overview • Estimation for each part • The optimization of the model • Results • Data Collection • Evaluation • Conclusion

24. Conclusion • Proposed a personalized recommendation model based on users’ historical behavior. • Users’ preference profile extracted from historical behavior, with the help of content from user model and paper information. • Provide recommendation service with generation model • Random walk model in original model to helping correlation transformation between users.

25. Thank you! Q&A

26. Appendix • MAP • M is size of recommended paper set, p (j) is the accuracy of first j recommended papers, l (j) is label information. C (di) is the total number of related papers to the viewed one di. • NDCG • r(i) refers to the relevant grade of ith paper and Zn is a normalized parameter, which assures and the values NDCG@n of top results add up to 1.