Scientific Paper Recommendation Emphasizing Each Researcher’s

Most Recent Research Topic

Kazunari Sugiyama8th January, 2010

2

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)

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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]

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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]

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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]

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Proposed Method

• System Overview• Construction of User Profile– Junior Researchers– Mid-level or Senior Researchers

• Construction of Feature Vectors for Candidate Papers to Recommend

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System Overview

Researcher

Candidate papers to recommend

userP

(1) Construct user profile from each researcher’s past papers

(2) Compute similarity between

userP

(3) Recommend papers with high similarity

),,1( tjjrecp F

1recpF to jrecpF

and

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Junior Researchers’ Published Papers

1p

(‘09)

1p

References

(‘06) (‘02) (‘07)

11 refp 21 refp lrefp 1

Relation between reference papersand 1p

[No published papers in the past]

(‘09)11 refpW 21 refpW lrefpW 1

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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)

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1p 2p ip np

(‘02) (‘03) (‘05) (‘09)

ip

References

(‘06) (‘07) (‘09)

ipcp 1ipcp 2

ik pcp

(‘03) (‘01) (‘04)

1refip 2refip lrefip

old new

Mid-level or senior researchers’ published papers

Relation between citation or referencepapers and ip

(‘05)1refipW

2refipW lrefipW

ipcpW 1 ipcpW 2

ipkcp

W

1npW 2npW inpW

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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)

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System Overview

Researcher

Candidate papers to recommend

userP

(1) Construct user profile from each researcher’s past papers

(2) Compute similarity between

userP

(3) Recommend papers with high similarity

),,1( tjjrecp F

1recpF to jrecpF

and

TF-IDF

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Experiment

• Experimental Data– DBLP papers for each researcher– ACL Anthology• 597 papers published in 2000 - 2006

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Experiment

• Evaluation Measure– Normalized Discounted Cumulative Gain (NDCG)• NDCG@5, NDCG@10

– Mean Reciprocal Rank (MRR)

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Experimental Results

• Junior Researchers• Mid-level or Senior Researchers

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Recommendation Accuracy for Junior Researchers

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[NDCG@5] [NDCG@10]

[MRR]

*

*

*

* : statistically significant for p < 0.05

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Recommendation Accuracy for Mid-level or Senior Researchers

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[NDCG@5] [NDCG@10]

[MRR]

**

*

*

** : statistically significant for p < 0.01

* : statistically significant for p < 0.05

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[NDCG@5] [NDCG@10]

[MRR]

+

*

+ : statistically significant for p < 0.05

+

+

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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: 0.2• NDCG@5: 0.521, NDCG@10: 0.459, MRR: 0.624

• 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 : 0.4• NDCG@5: 0.540, NDCG@10: 0.518, MRR: 0.812