Papers › Towards Knowledge-Based Recommender Dialog System
Towards Knowledge-Based Recommender Dialog System
Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Recommendation Systems | ReDial | KBRD | Recall@1 | 0.03 | #7 of 8 | Archive leaderboard | report |
| Recommendation Systems | ReDial | KBRD | Recall@10 | 0.163 | #7 of 8 | Archive leaderboard | report |
| Recommendation Systems | ReDial | KBRD | Recall@50 | 0.338 | #7 of 8 | Archive leaderboard | report |
| Text Generation | ReDial | KBRD | Distinct-3 | 0.3 | #5 of 5 | Archive leaderboard | report |
| Text Generation | ReDial | KBRD | Distinct-4 | 0.45 | #5 of 5 | Archive leaderboard | report |
| Text Generation | ReDial | KBRD | Perplexity | 17.9 | #5 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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