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.
@article{arxiv.1908.05391,
title = {Towards Knowledge-Based Recommender Dialog System},
author = {Qibin Chen and Junyang Lin and Yichang Zhang and Ming Ding and Yukuo Cen and Hongxia Yang and Jie Tang},
journal= {arXiv preprint arXiv:1908.05391},
year = {2019}
}