Social media filtering based on collaborative tagging in semantic space

Authors:
Heung-Nam Kim;Andrew Roczniak;Pierre Lévy;Abdulmotaleb Saddik
Affiliations:
Collective Intelligence Lab, University of Ottawa, Ottawa, Canada and Multimedia Communication Research Lab, University of Ottawa, Ottawa, Canada;Collective Intelligence Lab, University of Ottawa, Ottawa, Canada;Collective Intelligence Lab, Canada Research Chair in Collective Intelligence, University of Ottawa, Ottawa, Canada;Multimedia Communication Research Lab, University of Ottawa, Ottawa, Canada
Venue:
Multimedia Tools and Applications
Year:
2012

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Abstract

We propose a semantic collaborative filtering method to enhance recommendation quality derived from user-generated tags. Social tagging is employed as an approach in order to grasp and filter users' preferences for items. In addition, we explore several advantages of semantic tagging for ambiguity, synonymy, and semantic interoperability, which are notable challenges in information filtering. The proposed approach first determines semantically similar users using social tagging and subsequently discovers semantically relevant items for each user. Experimental results show that our method offers significant advantages both in terms of improving the recommendation quality and in dealing with ambiguity, synonymy, and interoperability issues.