A semantic model for social recommender systems

Authors:
Heung-Nam Kim;Andrew Roczniak;Pierre Lévy;Abdulmotaleb El-Saddik
Affiliations:
,Collective Intelligence Lab, University of Ottawa;Collective Intelligence Lab, University of Ottawa;Collective Intelligence Lab, University of Ottawa;Multimedia Communication Research Lab, University of Ottawa
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

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Abstract

Social recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of items suited to their needs To provide proper recommendations to users, social recommender systems require accurate models of characteristics, interests and needs for each user In this paper, we introduce a new model capturing semantics of user-generated tags and propose a social recommender system that is incorporated with the semantics of the tags Our approach first determines semantically similar items by utilizing semantic-oriented tags and secondly discovers semantically relevant items that are more likely to fit users' needs.