通过用户行为识别高决策成本搜索词，提升电商购物体验
Identifying High Consideration E-Commerce Search Queries
- 基于用户行为与商品信息构建查询排序模型
- 人工评估准确率达96%，优于人工筛选
- 适合电商推荐系统优化与精准营销
在电商场景中，高决策成本的搜索任务通常需要用户进行复杂细致的决策，并投入大量研究精力。本文提出一种基于参与度的查询排序（EQR）方法，用于识别高考虑度（HC）查询。该方法聚焦于查询层面的行为特征、财务信息和商品目录数据，而非流行度信号，以预测用户在搜索商品知识内容时的潜在参与程度。我们提出了一种高效且可扩展的EQR实现方法，并通过离线实验验证其出色排序性能。人工评估显示，模型识别出的高考虑度查询精确率达96%。该模型已上线商用，实际表现优于人工选取的查询，在用户参与度等下游指标上均有提升。
原文摘要 · Abstract (English)
In e-commerce, high consideration search missions typically require careful and elaborate decision making, and involve a substantial research investment from customers. We consider the task of identifying High Consideration (HC) queries. Identifying such queries enables e-commerce sites to better serve user needs using targeted experiences such as curated QA widgets that help users reach purchase decisions. We explore the task by proposing an Engagement-based Query Ranking (EQR) approach, focusing on query ranking to indicate potential engagement levels with query-related shopping knowledge content during product search. Unlike previous studies on predicting trends, EQR prioritizes query-level features related to customer behavior, finance, and catalog information rather than popularity signals. We introduce an accurate and scalable method for EQR and present experimental results demonstrating its effectiveness. Offline experiments show strong ranking performance. Human evaluation shows a precision of 96% for HC queries identified by our model. The model was commercially deployed, and shown to outperform human-selected queries in terms of downstream customer impact, as measured through engagement.
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