Recommandation mobile, sensible au contexte de contenus évolutifs: Contextuel-E-Greedy

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 content dynamicity. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation tradeoff and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. We conduct evaluations with real online event log data from a computer science compagny. The experimental results demonstrate that our algorithm outperforms surveyed algorithms. MOTS-CLÉS : système de recommandation, exploration/exploitation, apprentissage automatique