Chapter

Neural Information Processing

Volume 7665 of the series Lecture Notes in Computer Science pp 324-331

A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System

  • Djallel BouneffoufAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, Télécom SudParis, UMR CNRS Samovar
  • , Amel BouzeghoubAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, Télécom SudParis, UMR CNRS Samovar
  • , Alda Lopes GançarskiAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, Télécom SudParis, UMR CNRS Samovar

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Abstract

Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.

Keywords

Recommender system Machine learning Exploration/exploitation dilemma Artificial intelligence