Abstract
Query classification is a fundamental task in an e-commerce search engine, which assigns one or multiple predefined product categories in response to each search query. Taking click-through logs as training data in deep learning methods is a common and effective approach for query classification. However, the frequency distribution of queries typically has long-tail property, which means that there are few logs for most of the queries. The lack of reliable user feedback information results in worse performance of long-tail queries compared with frequent queries. To solve the above problem, we propose a novel method that leverages an auxiliary module to enhance the representations of long-tail queries by taking advantage of reliable supervised information of variant frequent queries. The long-tail queries are guided by the contrastive loss to obtain category-aligned representations in the auxiliary module, where the variant frequent queries serve as anchors in the representation space. We train our model with real-world click data from AliExpress and conduct evaluation on both offline labeled data and online AB test. The results and further analysis demonstrate the effectiveness of our proposed method.- Anthology ID:
- 2022.ecnlp-1.17
- Volume:
- Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Shervin Malmasi, Oleg Rokhlenko, Nicola Ueffing, Ido Guy, Eugene Agichtein, Surya Kallumadi
- Venue:
- ECNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 141–150
- Language:
- URL:
- https://aclanthology.org/2022.ecnlp-1.17
- DOI:
- 10.18653/v1/2022.ecnlp-1.17
- Cite (ACL):
- Lvxing Zhu, Hao Chen, Chao Wei, and Weiru Zhang. 2022. Enhanced Representation with Contrastive Loss for Long-Tail Query Classification in e-commerce. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 141–150, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Enhanced Representation with Contrastive Loss for Long-Tail Query Classification in e-commerce (Zhu et al., ECNLP 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.ecnlp-1.17.pdf