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Optimizing an Utility Function for Exploration / Exploitation Trade-off in Context-Aware Recommender System
[article]
2014
arXiv
pre-print
In this paper, we develop a dynamic exploration/ exploitation (exr/exp) strategy for contextual recommender systems (CRS). Specifically, our methods can adaptively balance the two aspects of exr/exp by automatically learning the optimal tradeoff. This consists of optimizing a utility function represented by a linearized form of the probability distributions of the rewards of the clicked and the non-clicked documents already recommended. Within an offline simulation framework we apply our
arXiv:1303.0485v2
fatcat:mtimh2qxmzgl7kcskwlotfdgxq