  Conversational recommender system (CRS), which combines the techniques of
dialogue system and recommender system, has obtained increasing interest
recently. In contrast to traditional recommender system, it learns the user
preference better through interactions (i.e. conversations), and then further
boosts the recommendation performance. However, existing studies on CRS ignore
to address the relationship among attributes, users, and items effectively,
which might lead to inappropriate questions and inaccurate recommendations. In
this view, we propose a knowledge graph based conversational recommender system
(referred as KG-CRS). Specifically, we first integrate the user-item graph and
item-attribute graph into a dynamic graph, i.e., dynamically changing during
the dialogue process by removing negative items or attributes. We then learn
informative embedding of users, items, and attributes by also considering
propagation through neighbors on the graph. Extensive experiments on three real
datasets validate the superiority of our method over the state-of-the-art
approaches in terms of both the recommendation and conversation tasks.
