Papers by Shumpei Sano
Search Query Embeddings via User-behavior-driven Contrastive Learning (2025.naacl-industry)
Copied to clipboard
Sosuke Nishikawa, Jun Hirako, Nobuhiro Kaji, Koki Watanabe, Hiroki Asano, Souta Yamashiro, Shumpei Sano
| Challenge: | Existing approaches to embed search queries are limited due to shortness and surface-level variations. |
| Approach: | They propose a user-behavior-driven contrastive learning approach which directly aligns query embeddings according to user intent. |
| Outcome: | The proposed model outperforms state-of-the-art text embedding models on real-world QU tasks while minimizing lexical similarities. |