In e-commerce, head queries account for the vast majority of gross merchandise sales and improvements to head queries are highly impactful to the business. While most supervised approaches to search perform better in head queries vs. tail queries, we propose a method that further improves head query performance dramatically. We propose XWalk, a random-walk based graph approach to candidate retrieval for product search that borrows from recommendation system techniques. XWalk is highly efficient to train and inference in a large-scale high traffic e-commerce setting, and shows substantial improvements in head query performance over state-of-the-art neural retreivers. Ensembling XWalk with a neural and/or lexical retriever combines the best of both worlds and the resulting retrieval system outperforms all other methods in both offline relevance-based evaluation and in online A/B tests.

翻译：摘要：在电子商务中，头部查询占据商品交易总额的绝大部分，针对头部查询的改进对业务具有显著影响。尽管大多数有监督搜索方法在头部查询上的表现优于尾部查询，本文提出了一种能进一步提升头部查询性能的方法。我们提出了XWalk——一种借鉴推荐系统技术的基于随机游走的图方法，用于商品搜索中的候选检索。XWalk在大规模高流量电商场景中训练与推理效率极高，且在头部查询性能上显著优于现有最先进的神经检索器。将XWalk与神经和/或词法检索器集成可结合两者优势，最终检索系统在离线相关性评估和在线A/B测试中均优于所有其他方法。