Recommendation Systems On Redial
평가 지표
Recall@1
Recall@10
Recall@50
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Recall@1 | Recall@10 | Recall@50 | Paper Title | Repository |
|---|---|---|---|---|---|
| KERL | 0.056 | 0.217 | 0.426 | Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems | |
| KBRD | 0.03 | 0.163 | 0.338 | Towards Knowledge-Based Recommender Dialog System | |
| C2CRS | 0.053 | 0.233 | 0.407 | C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System | |
| CR-Walker | 0.04 | 0.187 | 0.376 | CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation | |
| CRFR | 0.04 | 0.202 | 0.399 | CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs | - |
| UCCR | - | 0.2161 | 0.4258 | User-Centric Conversational Recommendation with Multi-Aspect User Modeling | - |
| KGSF | 0.039 | 0.183 | 0.378 | Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion | |
| UniCRS | 0.051 | 0.224 | 0.428 | Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning |
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