  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 algorithms to a CRS and conduct an evaluation
with real event log data. The experimental results and detailed analysis
demonstrate that our algorithms outperform existing algorithms in terms of
click-through-rate (CTR).
