Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity
Alireza Bagheri Garakani, Fan Yang, Wen-Yu Hua, Yetian Chen, Michinari Momma, Jingyuan Deng, Yan Gao, Yi Sun
Abstract
Ensuring relevance quality in product search is a critical task as it impacts the customer’s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term. In this work we leverage a high-precision cross-encoder BERT model for semantic similarity between customer query and products and survey its effectiveness for three ranking applications where offline-generated scores could be used: (1) as an offline metric for estimating relevance quality impact, (2) as a re-ranking feature covering head/torso queries, and (3) as a training objective for optimization. We present results on effectiveness of this strategy for the large e-commerce setting, which has general applicability for choice of other high-precision models and tasks in ranking.- Anthology ID:
- 2022.ecnlp-1.6
- Volume:
- Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ECNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 44–48
- Language:
- URL:
- https://aclanthology.org/2022.ecnlp-1.6
- DOI:
- 10.18653/v1/2022.ecnlp-1.6
- Cite (ACL):
- Alireza Bagheri Garakani, Fan Yang, Wen-Yu Hua, Yetian Chen, Michinari Momma, Jingyuan Deng, Yan Gao, and Yi Sun. 2022. Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 44–48, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity (Bagheri Garakani et al., ECNLP 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.ecnlp-1.6.pdf