  This paper studies the problem of learning interactive recommender systems
from logged feedbacks without any exploration in online environments. We
address the problem by proposing a general offline reinforcement learning
framework for recommendation, which enables maximizing cumulative user rewards
without online exploration. Specifically, we first introduce a probabilistic
generative model for interactive recommendation, and then propose an effective
inference algorithm for discrete and stochastic policy learning based on logged
feedbacks. In order to perform offline learning more effectively, we propose
five approaches to minimize the distribution mismatch between the logging
policy and recommendation policy: support constraints, supervised
regularization, policy constraints, dual constraints and reward extrapolation.
We conduct extensive experiments on two public real-world datasets,
demonstrating that the proposed methods can achieve superior performance over
existing supervised learning and reinforcement learning methods for
recommendation.
