Towards Knowledge-Based Recommender Dialog System

TL;DR

This paper introduces KBRD, an integrated knowledge-based dialog and recommender system that enhances both recommendation accuracy and dialog quality through mutual benefits and knowledge grounding.

Contribution

The paper presents a novel end-to-end framework combining recommender and dialog systems with knowledge grounding, improving performance over baselines.

Findings

Significant improvements in dialog generation quality.

Enhanced recommendation accuracy due to knowledge integration.

Mutual benefits observed between dialog and recommendation components.

Abstract

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.