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Exploration / Exploitation Trade-Off in Mobile Context-Aware Recommender Systems
Djallel Bouneffouf, Amel Bouzeghoub, and Alda Lopes Gançarski
Department of Computer Science, Télécom SudParis, UMR CNRS Samovar, 91011, Evry Cedex, FranceDjallel.Bouneffouf@it-sudparis.eu
Amel.Bouzeghoub@it-sudparis.eu
Alda.Gancarski@it-sudparis.eu
Abstract. The contextual bandit problem has been studied in the recommender system community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles this problem by modeling the Mobile Context-Aware Recommender Systems (MCRS) as a contextual bandit algorithm and it is based on dynamic exploration/exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demonstrate that our algorithm outperforms surveyed algorithms.
Keywords: recommender system, machine learning, exploration/exploitation dilemma, artificial intelligence
LNAI 7691, p. 591 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012