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Communication Dans Un Congrès Année : 2013

Risk-Aware recommender systems

Résumé

Context-Aware Recommender Systems can naturally be modelled as an exploration/exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr/exp behaviour

Dates et versions

hal-01257882 , version 1 (18-01-2016)

Identifiants

Citer

Djallel Bouneffouf, Amel Bouzeghoub, Alda Lopes Gancarski. Risk-Aware recommender systems. ICONIP 2013 : 20th International Conference on Neural Information Processing, Nov 2013, Daegu, South Korea. pp.57 - 65, ⟨10.1007/978-3-642-42054-2_8⟩. ⟨hal-01257882⟩
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