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

  • Djallel Bouneffouf
  • Amel Bouzeghoub
  • Alda Lopes Gançarski
Conference paper

DOI: 10.1007/978-3-642-34487-9_40

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7665)
Cite this paper as:
Bouneffouf D., Bouzeghoub A., Gançarski A.L. (2012) A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System. In: Huang T., Zeng Z., Li C., Leung C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg

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 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Djallel Bouneffouf
    • 1
  • Amel Bouzeghoub
    • 1
  • Alda Lopes Gançarski
    • 1
  1. 1.Department of Computer ScienceTélécom SudParis, UMR CNRS SamovarEvry CedexFrance

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