Taking Advantage of Semantics in Recommendation Systems

Authors:
Victor Codina;Luigi Ceccaroni
Affiliations:
Departament de Llenguatges i Sistemes Informàtics (LSI), Universitat Politècnica de Catalunya (UPC), Campus Nord, Edif. Omega, C. Jordi Girona, 1-3, 08034 Barcelona, Spain;Departament de Llenguatges i Sistemes Informàtics (LSI), Universitat Politècnica de Catalunya (UPC), Campus Nord, Edif. Omega, C. Jordi Girona, 1-3, 08034 Barcelona, Spain
Venue:
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Year:
2010

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Abstract

Recommendation systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommendation systems, content-based recommendation systems and a few hybrid systems. We propose a semantic framework to overcome common limitations of current systems. We present a system whose representations of items and user-profiles are based on concept taxonomies in order to provide personalized recommendation and services. The recommender incorporates semantics to enhance (1) user modeling by applying a domain-based inference method, and (2) recommendation by applying a semantic-similarity method. We show that semantics can often be used to overcome information scarcity. Experiments on movie-data from Netflix show that systems incorporating semantics produce significantly better quality recommendations than content-based ones.