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Communication Dans Un Congrès Année : 2012

A contextual-bandit algorithm for mobile context-aware recommender system

Résumé

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 use'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.
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Dates et versions

hal-00753401 , version 1 (19-11-2012)

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Citer

Djallel Bouneffouf, Amel Bouzeghoub, Alda Lopes Gançarski. A contextual-bandit algorithm for mobile context-aware recommender system. ICONIP '12 : The 19th International Conference on Neural Information Processing, Nov 2012, Doha, Qatar. pp.324-331, ⟨10.1007/978-3-642-34487-9_40⟩. ⟨hal-00753401⟩
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