OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion
Xian Guo, Ben Chen, Siyuan Wang, Ying Yang, Chenyi Lei, Yuqing Ding, Han Li

TL;DR
OneSug is an end-to-end generative framework for e-commerce query suggestion that improves efficiency and performance by unifying the suggestion process and capturing user preferences, leading to significant online improvements.
Contribution
It introduces the first end-to-end generative model for e-commerce query suggestion, integrating semantic enrichment, unified generation, and behavior-aware ranking.
Findings
Effective query suggestion demonstrated on large-scale industry datasets.
Statistically significant online improvements in CTR, order, and revenue.
Successful deployment on Kuaishou platform for over a month.
Abstract
Query suggestion plays a crucial role in enhancing user experience in e-commerce search systems by providing relevant query recommendations that align with users' initial input. This module helps users navigate towards personalized preference needs and reduces typing effort, thereby improving search experience. Traditional query suggestion modules usually adopt multi-stage cascading architectures, for making a well trade-off between system response time and business conversion. But they often suffer from inefficiencies and suboptimal performance due to inconsistent optimization objectives across stages. To address these, we propose OneSug, the first end-to-end generative framework for e-commerce query suggestion. OneSug incorporates a prefix2query representation enhancement module to enrich prefixes using semantically and interactively related queries to bridge content and business…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Web Data Mining and Analysis · Recommender Systems and Techniques
