A Paper Recommender for Scientific Literatures Based on Semantic Concept Similarity

Authors:
Ming Zhang;Weichun Wang;Xiaoming Li
Affiliations:
School of Electronics Engineering and Computer Science, Peking University, P.R. China;School of Electronics Engineering and Computer Science, Peking University, P.R. China;School of Electronics Engineering and Computer Science, Peking University, P.R. China
Venue:
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Year:
2008

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Abstract

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 dataset crawled from CiteULike, with satisfied experiment results.