Scientific Paper Recommendation Emphasizing Each Researcher’s Most Recent Research Topic Kazunari Sugiyama 8 th January, 2010

Introduction The number of published scientific papers continues to grow. Users of digital library suffer from finding papers relevant to their information needs. Recommendation systems are promising approach to address each user’s interest. – Mid-level or senior researchers Several different research interests based on several years experience – Junior researchers Quite small publication list (too short to construct user profile) 2

Related Work Improvement in Ranking of Digital Library – ISI impact factor (ISI IF) Papers with high impact and low impact are treated equally. Its ranking are biased towards popularity. – Improved approach “Focused PageRank” [Sun and Giles, ECIR’07] “FutureRank” [Sayyadi and Getoor, SIAM-Data Mining, ‘09] Weighted PageRank, Y-factor (product of ISI IF and weighted PageRank) [Bollen et al., Journal of Scientometrics ‘06] “Scientific gems” [Chen et al., Journal of Informetrics ‘07] 3

Related Work Recommendation Systems in Digital Library – Recommend citations [McNee et al., CSCW’02] – Recommend papers by combining collaborative filtering and content-based filtering [Torres et al., JCDL’04] – Recommend paper s ranking-oriented collaborative filtering [Yang et al., JCDL’09] 4

Related Work Construction of Robust User Profile in Recommendation Systems – Content-based approach Frequent patterns obtained by click-history [Kim et al., ICADL’08] News recommender system [Das et al., WWW’07], [Chu and Park, WWW’09] Long-term search history [Shen et al., SIGIR’05], [Tan et al., KDD’06], [White et al., SIGIR’09] 5

Proposed Method System Overview Construction of User Profile – Junior Researchers – Mid-level or Senior Researchers Construction of Feature Vectors for Candidate Papers to Recommend 6

System Overview Researcher Candidate papers to recommend (1) Construct user profile from each researcher’s past papers (2) Compute similarity between (3) Recommend papers with high similarity to and 7

Junior Researchers’ Published Papers (‘09) References (‘06) (‘02)(‘07) Relation between reference papers and [No published papers in the past] (‘09) 8

Weighting Schemes for Junior Researchers’ Published Papers Linear Combination (LC) Similarity between the most recent paper and others (SIM) Reciprocal of the difference between published year of the most recent paper and that of other papers (RPY) 9

(‘02) (‘03) (‘05)(‘09) References (‘06) (‘07) (‘09) (‘03) (‘01)(‘04) old new Mid-level or senior researchers’ published papers Relation between citation or reference papers and (‘05) 10

Weighting Schemes for Mid-level or Senior Researchers’ Published Papers Linear Combination (LC) Similarity between the most recent paper and others (SIM) Reciprocal of the difference between published year of the most recent paper and that of other papers (RPY) Forgetting factor (FF) 11

System Overview Researcher Candidate papers to recommend (1) Construct user profile from each researcher’s past papers (2) Compute similarity between (3) Recommend papers with high similarity to and TF-IDF 12

Experiment Experimental Data – DBLP papers for each researcher – ACL Anthology 597 papers published in

Experiment Evaluation Measure – Normalized Discounted Cumulative Gain (NDCG) – Mean Reciprocal Rank (MRR) 14

Experimental Results Junior Researchers Mid-level or Senior Researchers 15

Recommendation Accuracy for Junior Researchers 16

[MRR] * * * * : statistically significant for p <

Recommendation Accuracy for Mid-level or Senior Researchers 18

[MRR] * * * * ** : statistically significant for p < 0.01 * : statistically significant for p <

[MRR] + * + : statistically significant for p <

Conclusion Recommendation system of scientific papers for junior researchers, and mid-level or senior researchers Junior researcher – User profile constructed using the most recent paper and its pruned reference paper gives the best recommendation accuracy. Threshold of pruning: , 0.459, MRR: Mid-level or senior researcher – User profile constructed using papers published within 3 years and its pruned citation and reference papers gives the best recommendation accuracy. Threshold of pruning : , 0.518, MRR: