  The chit-chat-based conversational recommendation systems (CRS) provide item
recommendations to users through natural language interactions. To better
understand user's intentions, external knowledge graphs (KG) have been
introduced into chit-chat-based CRS. However, existing chit-chat-based CRS
usually generate repetitive item recommendations, and they cannot properly
infuse knowledge from KG into CRS to generate informative responses. To remedy
these issues, we first reformulate the conversational recommendation task to
highlight that the recommended items should be new and possibly interested by
users. Then, we propose the Knowledge-Enriched Conversational Recommendation
System (KECRS). Specifically, we develop the Bag-of-Entity (BOE) loss and the
infusion loss to better integrate KG with CRS for generating more diverse and
informative responses. BOE loss provides an additional supervision signal to
guide CRS to learn from both human-written utterances and KG. Infusion loss
bridges the gap between the word embeddings and entity embeddings by minimizing
distances of the same words in these two embeddings. Moreover, we facilitate
our study by constructing a high-quality KG, \ie The Movie Domain Knowledge
Graph (TMDKG). Experimental results on a large-scale dataset demonstrate that
KECRS outperforms state-of-the-art chit-chat-based CRS, in terms of both
recommendation accuracy and response generation quality.
