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Scholarly Paper Recommendation via User’s Recent Research Interests

Scholarly Paper Recommendation via User’s Recent Research Interests. Kazunari Sugiyama, Min-Yen Kan. National University of Singapore. Introduction. “Information Overload”. Digital Contents. Documents. Images. Movies. Introduction. Email alert. RSS feeds. Users are required

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Scholarly Paper Recommendation via User’s Recent Research Interests

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  1. Scholarly Paper Recommendation via User’s Recent Research Interests Kazunari Sugiyama, Min-Yen Kan National University of Singapore

  2. Introduction “Information Overload” • Digital Contents Documents Images Movies WING, NUS

  3. Introduction • Email alert • RSS feeds Users are required explicit inputs of their interests. WING, NUS

  4. Introduction • Our aims • Provide recommendation of papers by using latent information about each user’s research interests • Historical and current publication lists Users are not required explicit inputs of their interests. WING, NUS

  5. Related Work • Improving Ranking in Digital Library • Ranking Search Results ISI impact factor [Garfield, ‘79] = Low impact High impact • Recent works introduce PageRank to weight • and control for the impact of papers [Sun and Giles, ECIR’07] [Krapivin and Marchese, ICADL’08], [Sayyadi and Getoor, SIAM Data Mining, ‘09] WING, NUS

  6. Related Work • Improving Ranking in Digital Library • Measuring the Importance of Scholarly Papers ISI impact factor [Garfield, ‘79] Popularity biased PageRank also contributes to control the popularity bias [Bollen et al., Scientometrics’06] [Chen et al., Informetrics’07] WING, NUS

  7. Related Work • Recommendation in Digital Libraries • Collaborative Filtering Approach • [McNee et al., CSCW’02] • [Yang et al., JCDL’09] • Hybrid Approach of Collaborative Filtering • and Content-based filtering • [Torres et al., JCDL’04] • PageRank-based Approach • [Gori and Pucci, WI’06] WING, NUS

  8. Related Work • Robust User Profile Construction in Recommendation Systems • Web Search Results • [Teevan et al., SIGIR’05] • [White et al., SIGIR’09] • Dynamic Content such as news • [Shen et al., SIGIR’05] • [Tan et al., KDD’06] • [Chu and Park, WWW’09] • Abstracts of Scholarly Papers • [Kim et al., ICADL’08] WING, NUS

  9. Proposed Method • Junior researchers • Senior researchers WING, NUS

  10. (1) User Profile Construction • Junior Researchers WING, NUS

  11. (1) User Profile Construction • Senior Researchers WING, NUS

  12. Linear Combination Weighting Scheme (LC) ( …, digital, library, recommendation, …) ( …, digital, library, recommendation, …) (…, 0.25, 0.13, 0.47, …) (…, 0.61, 0.72, 0.85, …) ( …, digital, library, recommendation, …) (…, 0.53, 0.38, 0.62, …) References 1 1 WING, NUS

  13. Cosine Similarity Weighting Scheme (SIM) ( …, digital, library, recommendation, …) ( …, digital, library, recommendation, …) (…, 0.25, 0.13, 0.47, …) (…, 0.61, 0.72, 0.85, …) Similarity: 0.36 ( …, digital, library, recommendation, …) (…, 0.53, 0.38, 0.62, …) References Similarity: 0.54 0.36 0.54 WING, NUS

  14. Reciprocal of the Difference Between Published Years Weighting Scheme (RPY) ( …, digital, library, recommendation, …) ( …, digital, library, recommendation, …) (…, 0.25, 0.13, 0.47, …) (…, 0.61, 0.72, 0.85, …) (‘07) RPY: 1/2=0.50 Difference of published years: 2 ( …, digital, library, recommendation, …) (…, 0.53, 0.38, 0.62, …) (‘05) References Difference of published years: 4 RPY: 1/4=0.25 (‘01) 0.50 0.25 WING, NUS

  15. Forgetting Factor Weighting Scheme (FF) Publication list new old (‘05) (‘10) (‘02) (‘03) ] [ : forgetting coefficient ) (e.g., WING, NUS

  16. (2) Feature Vector Construction for Candidate Papers • Basically, TF-IDF • Also use information about citation and reference papers References WING, NUS

  17. (3) Recommendation of Papers • Compute cosine similarity • Then, recommend the top n papers to the user • n=5,10 WING, NUS

  18. Experiments • Experimental Data • Researchers WING, NUS

  19. Experiments • Experimental Data • Candidate Papers to Recommend • ACL Anthology Reference Corpus • [Bird et al., LREC’08] Information about citation and reference papers References <= <= WING, NUS

  20. Experiments • Evaluation Measure • NDCG@5, 10 • Gives more weight to highly ranked items • MRR • Provides insight in the ability to return a relevant item at the top of the ranking WING, NUS

  21. Junior Researchers • The most recent paper only WING, NUS

  22. Pruning of Reference Papers Threshold: 0.3 References sim:0.53 sim:0.27 sim:0.43 sim:0.16 sim:0.38 WING, NUS

  23. Pruning of Reference Papers Threshold: 0.3 References sim:0.53 sim:0.27 sim:0.43 sim:0.16 sim:0.38 Weighting scheme • SIM • RPY • LC WING, NUS

  24. Junior Researchers • The most recent paper with pruning its reference papers [NDCG5] [MRR] WING, NUS

  25. Senior Researchers • The most recent paper only WING, NUS

  26. Pruning of Citation and Reference Papers sim:0.32 sim:0.27 sim:0.42 sim:0.25 sim:0.13 Threshold: 0.3 References sim:0.18 sim:0.58 sim:0.22 sim:0.36 sim:0.45 WING, NUS

  27. Pruning of Citation and Reference Papers sim:0.33 sim:0.27 sim:0.42 sim:0.25 sim:0.13 Threshold: 0.3 References Weighting scheme • SIM • RPY sim:0.18 sim:0.58 sim:0.22 sim:0.36 sim:0.45 • LC WING, NUS

  28. Senior Researchers • The most recent paper with pruning its citation • and reference papers [NDCG5] [MRR] WING, NUS

  29. Senior Researchers • Past published papers [NDCG5] [MRR] WING, NUS

  30. Conclusion • Propose a generic model towards recommending scholarly papers relevant to junior and senior researcher’s interests • Use past publications to capture the researcher’s interests • Our user model also incorporate its citation and reference papers (neighboring papers) as context • (Also employ this scheme to characterize candidate • papers to recommend) • Achieve higher recommendation accuracy when our model prunes neighboring papers with low similarity • This scheme can enhance the signal of the original topic of • the paper WING, NUS

  31. Future Work • We plan to develop: • Methods for helping recommend interdisciplinary papers that could encourage a push to new frontiers for senior researchers • Methods for recommending papers that are easier to understand to quickly acquire knowledge about intended research Thank you very much! WING, NUS

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