Exploration/exploitation trade-off in mobile context-aware recommender systems

Djallel Bouneffouf (1, 2, 3, 4) , Amel Bouzeghoub (1, 2, 4) , Alda Lopes Gançarski (1, 2, 4)

1 TSP - INF - Département Informatique
2 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
3 NOMALYS
4 CNRS - Centre National de la Recherche Scientifique

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

Domains: Networking and Internet Architecture [cs.NI]

Djallel Bouneffouf, Amel Bouzeghoub, Alda Lopes Gançarski. Exploration/exploitation trade-off in mobile context-aware recommender systems. AI '12 : The Twenty-Fifth Australasian Joint Conferences on Artificial Intelligence, Dec 2012, Sydney, Australia. pp.591-601, ⟨10.1007/978-3-642-35101-3_50⟩. ⟨hal-00766969⟩