用三维度评估咨询价值，提升电商搜索个性化效果
Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
- 从场景范围、后续行为、时间衰减三方面评估咨询价值
- 在公开与商业数据集上均显著优于基线模型
- 适合做电商搜索个性化与智能客服系统优化
电商平台的个性化搜索日益依赖用户与AI助手的互动咨询。现有方法多基于语义相似度对齐历史咨询与当前查询，但该方式常无法准确捕捉咨询的真实价值。为此，本文提出一种咨询价值评估框架，从三个新视角衡量历史咨询价值：(1) 场景范围价值，(2) 后续行为价值，(3) 时间衰减价值。基于此，构建了VAPS模型，通过咨询-用户行为交互模块和显式对齐目标，有选择地引入高价值咨询。在公开与商业数据集上的实验表明，VAPS在检索与排序任务中均持续超越基线模型。
原文摘要 · Abstract (English)
Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of 'value' labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for personalization. To address this, we propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. Based on this, we introduce VAPS, a value-aware personalized search model that selectively incorporates high-value consultations through a consultation-user action interaction module and an explicit objective that aligns consultations with user actions. Experiments on both public and commercial datasets show that VAPS consistently outperforms baselines in both retrieval and ranking tasks.
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