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In this paper, we propose a novel end-toend 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 resultsdoi:10.18653/v1/d19-1189 dblp:conf/emnlp/ChenLZDCYT19 fatcat:3ibp46djazhhvfhhihddhqdyh4