On the recommending of citations for research papers

Sean M. McNee; Istvan Albert; Dan Cosley; Prateep Gopalkrishnan; Shyong K. Lam; Al Mamunur Rashid; Joseph A. Konstan; John Riedl

doi:10.1145/587078.587096

On the recommending of citations for research papers

Sean M. McNee, Istvan Albert, Dan Cosley, Prateep Gopalkrishnan, Shyong K. Lam, Al Mamunur Rashid, Joseph A. Konstan, John Riedl

Administration (CSENG)

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334 Scopus citations

Abstract

Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.

Original language	English (US)
Pages	116-125
Number of pages	10
DOIs	https://doi.org/10.1145/587078.587096
State	Published - 2002
Event	The eight Conference on Computer Supported Cooperative Work (CSCW 2002) - New Orleans, LA, United States Duration: Nov 16 2002 → Nov 20 2002

Other

Other	The eight Conference on Computer Supported Cooperative Work (CSCW 2002)
Country/Territory	United States
City	New Orleans, LA
Period	11/16/02 → 11/20/02

Keywords

Citation Graphs
Collaborative Filtering
Digital Libraries
Recommender Systems
ResearchIndex
Social Networks

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title = "On the recommending of citations for research papers",

abstract = "Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.",

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doi = "10.1145/587078.587096",

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AU - McNee, Sean M.

AU - Albert, Istvan

AU - Cosley, Dan

AU - Gopalkrishnan, Prateep

AU - Lam, Shyong K.

AU - Rashid, Al Mamunur

AU - Konstan, Joseph A.

AU - Riedl, John

PY - 2002

Y1 - 2002

N2 - Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.

AB - Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.

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KW - Collaborative Filtering

KW - Digital Libraries

KW - Recommender Systems

KW - ResearchIndex

KW - Social Networks

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ER -

On the recommending of citations for research papers

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