Towards Knowledge-Based Recommender Dialog System

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.

PDF Abstract IJCNLP 2019 PDF IJCNLP 2019 Abstract


Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Text Generation ReDial KBRD Perplexity 17.9 # 1
Distinct-3 0.3 # 5
Distinct-4 0.45 # 4
Recommendation Systems ReDial KBRD Recall@1 0.03 # 7
Recall@10 0.163 # 8
Recall@50 0.338 # 8


No methods listed for this paper. Add relevant methods here