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In this paper, we develop a dynamic exploration/ exploitation (exr/exp)strategy for contextual recommender systems (CRS). Specifically, our methodscan adaptively balance the two aspects of exr/exp by automatically learning theoptimal tradeoff. This consists of optimizing a utility function represented bya linearized form of the probability distributions of the rewards of theclicked and the non-clicked documents already recommended. Within an offlinesimulation framework we apply our algorithms to a CRS and conduct an evaluationwith real event log data. The experimental results and detailed analysisdemonstrate that our algorithms outperform existing algorithms in terms ofclick-through-rate (CTR).

Optimizing an Utility Function for Exploration / Exploitation Trade-off in Context-Aware Recommender System