  In this paper, we propose a novel end-to-end framework called KBRD, which
stands for Knowledge-Based Recommender Dialog System. It integrates the
recommender system and the dialog generation system. The dialog system can
enhance the performance of the recommendation system by introducing
knowledge-grounded information about users' preferences, and the recommender
system can improve that of the dialog generation system by providing
recommendation-aware vocabulary bias. Experimental results demonstrate that our
proposed model has significant advantages over the baselines in both the
evaluation of dialog generation and recommendation. A series of analyses show
that the two systems can bring mutual benefits to each other, and the
introduced knowledge contributes to both their performances.
