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.

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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 # 6
Recall@10 0.163 # 7
Recall@50 0.338 # 7


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