  Interactive recommender system (IRS) has drawn huge attention because of its
flexible recommendation strategy and the consideration of optimal long-term
user experiences. To deal with the dynamic user preference and optimize
accumulative utilities, researchers have introduced reinforcement learning (RL)
into IRS. However, RL methods share a common issue of sample efficiency, i.e.,
huge amount of interaction data is required to train an effective
recommendation policy, which is caused by the sparse user responses and the
large action space consisting of a large number of candidate items. Moreover,
it is infeasible to collect much data with explorative policies in online
environments, which will probably harm user experience. In this work, we
investigate the potential of leveraging knowledge graph (KG) in dealing with
these issues of RL methods for IRS, which provides rich side information for
recommendation decision making. Instead of learning RL policies from scratch,
we make use of the prior knowledge of the item correlation learned from KG to
(i) guide the candidate selection for better candidate item retrieval, (ii)
enrich the representation of items and user states, and (iii) propagate user
preferences among the correlated items over KG to deal with the sparsity of
user feedback. Comprehensive experiments have been conducted on two real-world
datasets, which demonstrate the superiority of our approach with significant
improvements against state-of-the-arts.
