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Personalized Movie Recommendation

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Handbook of Multimedia for Digital Entertainment and Arts

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Abstract

This article proposes a movie recommender system, named MoRe, which follows a hybrid approach that combines content-based and collaborative filtering. MoR’s performance is empirically evaluated upon the predictive accuracy of the algorithms as well as other important indicators such as the percentage of items that the system can actually predict (called prediction coverage) and the time required for generating predictions. The remainder of this article is organized as follows. The next section is devoted to the fundamental background of recommender systems describing the main recommendation techniques along with their advantages and limitations. Right after, we illustrate the MoRe system overview and in the section following, we describe in detail the algorithms implemented. The empirical evaluation results are then presented, while the final section provides a discussion about conclusions and future research.

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Authors and Affiliations

  1. ELTRUN, the e-Business Center, Department of Management Science and Technology, Athens University of Economics and Business, Athens, Greece

    George Lekakos, Matina Charami & Petros Caravelas

Authors
  1. George Lekakos
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  2. Matina Charami
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  3. Petros Caravelas
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Correspondence to George Lekakos .

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Editors and Affiliations

  1. Dept. Computer Science &, Florida Atlantic University, Glades Road 777, Boca Raton, 33431, U.S.A.

    Borko Furht

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Lekakos, G., Charami, M., Caravelas, P. (2009). Personalized Movie Recommendation. In: Furht, B. (eds) Handbook of Multimedia for Digital Entertainment and Arts. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-89024-1_1

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  • DOI: https://doi.org/10.1007/978-0-387-89024-1_1

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