  This paper studies recommender systems with knowledge graphs, which can
effectively address the problems of data sparsity and cold start. Recently, a
variety of methods have been developed for this problem, which generally try to
learn effective representations of users and items and then match items to
users according to their representations. Though these methods have been shown
quite effective, they lack good explanations, which are critical to recommender
systems. In this paper, we take a different route and propose generating
recommendations by finding meaningful paths from users to items. Specifically,
we formulate the problem as a sequential decision process, where the target
user is defined as the initial state, and the edges on the graphs are defined
as actions. We shape the rewards according to existing state-of-the-art methods
and then train a policy function with policy gradient methods. Experimental
results on three real-world datasets show that our proposed method not only
provides effective recommendations but also offers good explanations.
