Abstract

Nowadays, Recommender Systems (RS) are being widely and successfully used in online applications. A successful Recommender System can help in increasing the revenue of a web-site as well as helping it to maintain and increase its users. Until now, research in recommendation algorithms is mainly based on machine learning and AI techniques. In this article we aim to develop recommendation algorithms utilizing Operations Research (OR) methods that provide the ability to move towards an optimized set of items to be recommended. We focus on expressing the Collaborative Filtering Algorithm (CF or CFA) as a Greedy Construction Algorithm as well as implementing and testing a Collaborative Metaheuristic Algorithm (CMA) for providing recommendations. The empirical findings suggest that the recommendation problem can indeed be defined as an optimization problem, which provides new opportunities for the application of powerful and effective OR algorithms on recommendation problems.

Keywords

Recommender Systems Personalization algorithms Operational Research Metaheuristic 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thomas Asikis
    • 1
  • George Lekakos
    • 1
  1. 1.Department of Management Science and TechnologyAthens University of Economics and BusinessAthensGreece

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