Towards Knowledge-Based Recommender Dialog System
This work addresses the challenge of building more effective and interactive recommender systems for users, though it appears incremental as it combines existing components with knowledge integration.
The authors tackled the problem of integrating recommender and dialog systems by proposing KBRD, an end-to-end framework that uses knowledge-grounded information to enhance both recommendation and dialog generation, achieving significant advantages over baselines in evaluations.
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.