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. © 2008 Springer Berlin Heidelberg.
Author supplied keywords
Cite
CITATION STYLE
Zhang, M., Wang, W., & Li, X. (2008). A paper recommender for scientific literatures based on semantic concept similarity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5362 LNCS, pp. 359–362). Springer Verlag. https://doi.org/10.1007/978-3-540-89533-6_44
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
