On the Recommending of Citations for Research Papers

@article{citeulike:374169, 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.}, address = {New Orleans, Louisiana}, author = {Mcnee, Sean M. and Albert, Istvan and Cosley, Dan and Gopalkrishnan, Prateep and Lam, Shyong K. and Rashid, Al M. and Konstan, Joseph A. and Ried, John }, citeulike-article-id = {374169}, journal = {Proceedings of the 2002 ACM conference on Computer supported cooperative work}, keywords = {citations, collaborative\_filtering, personalization, recommender\_systems}, pages = {116--125}, posted-at = {2006-06-15 18:38:19}, priority = {0}, title = {On the Recommending of Citations for Research Papers}, url = {http://delivery.acm.org/10.1145/590000/587096/p116-mcnee.pdf?key1=587096\&\#38;key2=8783080311\&\#38;coll=GUIDE\&\#38;dl=GUIDE\&\#38;CFID=58966692\&\#38;CFTOKEN=81023010}, year = {2002} }

TY - JOUR ID - citeulike:374169 L3 - citeulike-article-id:374169 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. JF - Proceedings of the 2002 ACM conference on Computer supported cooperative work AD - New Orleans, Louisiana EP - 125 TI - On the Recommending of Citations for Research Papers SP - 116 KW - citations KW - collaborative_filtering KW - personalization KW - recommender_systems AU - Mcnee, Sean AU - Albert, Istvan AU - Cosley, Dan AU - Gopalkrishnan, Prateep AU - Lam, Shyong AU - Rashid, Al AU - Konstan, Joseph AU - Ried, John PY - 2002/// UR - http://delivery.acm.org/10.1145/590000/587096/p116-mcnee.pdf?key1=587096&key2=8783080311&coll=GUIDE&dl=GUIDE&CFID=58966692&CFTOKEN=81023010 ER -