Personalized Movie Recommendation

  • George Lekakos
  • Matina Charami
  • Petros Caravelas


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.


Recommender System Target User Collaborative Filter Mean Absolute Error Recommendation Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • George Lekakos
    • 1
  • Matina Charami
    • 1
  • Petros Caravelas
    • 1
  1. 1.ELTRUN, the e-Business Center, Department of Management Science and TechnologyAthens University of Economics and BusinessAthensGreece

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