通过用户行为与需求模式，解决电商搜索中模糊查询的意图识别问题。
IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search
- 结合用户历史行为与群体需求趋势，推断模糊查询背后的潜在意图。
- 用户历史搜索记录比群体数据和静态资料更准确预测性别、年龄等意图。
- 适合做电商搜索优化与个性化推荐系统的研发人员参考。
理解用户意图是电商搜索系统提供相关结果的基础。然而，大量真实查询存在信息不足（如“手表”或“衬衫”），缺乏性别、年龄等明确属性。这种模糊性对查询意图识别模型构成重大挑战，需准确推断隐含意图（如年龄、性别）以支持后续检索。我们提出 IntentTune 框架，通过两种方式解决模糊查询意图：(1) 用户级行为信号，包括搜索历史、浏览活动和画像特征；(2) 全体用户聚合的需求模式。在真实电商数据上的实验表明，仅依赖群体需求模式无法可靠推断意图；而用户级行为信号，尤其是先前搜索记录，在推断性别、年龄组、品类及尺码意图方面显著优于群体统计与静态画像信息。
原文摘要 · Abstract (English)
Understanding user intent is fundamental to delivering relevant search results in e-commerce. However, substantial fraction of real-world queries are under-specified (e.g., "watch" or "shirt"), lacking explicit attributes such as gender or age group. This ambiguity poses a significant challenge for query intent detection models in e-commerce search systems, which must accurately infer latent user intent (e.g., age, gender) to support effective downstream retrieval. We introduce IntentTune, a framework for resolving ambiguous or under-specified query intents by leveraging either (1) user-specific behavioral signals including search history, browsing activity, and profile attributes or (2) population-level demand patterns aggregated across all users. Through experiments on real-world e-commerce data, we first demonstrate that population-level demand patterns alone are insufficient to reliably infer intent in under-specified queries. We then demonstrate that user-specific behavioral signals -- particularly prior search queries -- outperform both population-level statistics and static profile information for inferring gender, age group, product category, and size intent from underspecified queries.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。