A Paper Recommender for Scientific Literatures Based on Semantic Concept Similarity

Recently, collaborative tagging has become more and more popular in the Web2.0 community, since tags in these Web2.0 systems reflect the specific content features of the resources. This paper presents a recommender for scientific literatures based on semantic concept similarity computed from the collaborative tags. User profiles and item profiles are presented by these semantic concepts, and neighbor users are selected using collaborative filtering. Then, content-based filtering approach is used to generate recommendation list from the papers these neighbor users tagged. The evaluation is carried out on a data-set crawled from CiteULike, with satisfied experiment results.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000262503100044&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Information SystemsComputer Science, Theory & MethodsInformation Science & Library ScienceCPCI-S(ISTP)CPCI-SSH(ISSHP)