发表机构
Peking University; Alibaba Group（北京大学; 阿里巴巴集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出行为到价值（B2V）任务及首个基准B2V-Bench，并构建B2V-Verifier模型，从电商行为轨迹中识别消费者价值观，多标签分类性能提升34%。
AI 中文摘要
人类价值观是塑造人类行为的深层动机取向。在电子商务中，它们揭示了用户购买决策背后的稳定驱动因素。与短期兴趣相比，消费者价值观更能解释用户在购买前如何评估产品。然而，消费者价值观通常隐含在复杂且碎片化的行为轨迹中，导致从电商行为中进行价值衡量在很大程度上尚未被充分探索。为此，我们提出了行为到价值（B2V）任务，旨在从电商行为轨迹中识别消费者价值观。围绕这一任务，我们首先构建了电商消费价值分类体系（ECVT），并基于匿名淘宝行为日志引入了B2V-Bench，这是首个B2V数据集和基准。B2V-Bench包含真实世界的购买决策片段，涵盖25种购买行为类型，以及每个片段中体现的相应消费者价值取向。为了提高消费者价值衡量的准确性，我们进一步提出了B2V-Verifier，一种基于价值验证调优的行为到价值衡量模型，该模型学习评估行为是否为每个价值推断提供充分证据。实验表明，B2V-Verifier优于强大的LLM基线，在多标签分类上提升了34%。数据集和代码将在录用后公开发布。
英文摘要
Human values are deep motivational orientations that shape human behaviors. In e-commerce, they reveal the stable drivers behind users' purchase decisions. Compared with short-term interests, consumer values better explain how users evaluate products before purchase. However, consumer values are often implicit in complex and fragmented behavioral trajectories, leaving value measurement from e-commerce behaviors largely underexplored. To this end, we propose the Behavior-to-Value (B2V) task, which aims to identify consumer values from e-commerce behavioral trajectories. Centered on this task, we first construct the E-commerce Consumption Value Taxonomy (ECVT) and introduce B2V-Bench, the first B2V dataset and benchmark, based on anonymized Taobao behavioral logs. B2V-Bench consists of real-world purchase decision episodes, covering 25 types of purchase behaviors, along with corresponding consumer value orientations manifested in each episode. To improve consumer value measurement accuracy, we further present B2V-Verifier, a behavior-to-value measurement model based on Value Verification Tuning, which learns to assess whether behaviors provide sufficient evidence for each value inference. Experiments show that B2V-Verifier outperforms strong LLM baselines, improving multi-label classification by 34\%. The dataset and code will be publicly released upon acceptance.
