用多模态模型从用户查询推断购买意图，提升电商检索准确率
Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap
- 融合点击流与商品目录数据，构建跨模态查询转换模型
- 在真实场景下优于现有方法，显著提升查询到商品的匹配精度
- 适合电商搜索、推荐系统优化方向的研究者和工程师
电商平台的点击流数据与商品目录包含关键的用户行为信息和产品知识。本文提出一种名为PINCER的多模态Transformer模型，利用上述数据源将用户的初始查询转化为伪商品表征。通过挖掘外部数据，模型可从有限查询中推断潜在购买意图，并捕捉与查询相关的商品特征。在受控环境和真实场景下的电商在线检索实验中，该模型均优于当前最优方法。消融实验证明，所提出的Transformer架构与集成学习策略有效挖掘了关键数据源，实现了购买意图推断、产品特征提取及查询到更精准伪商品表征的转化。
原文摘要 · Abstract (English)
E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that leverages the above data sources to transform initial user queries into pseudo-product representations. By tapping into these external data sources, our model can infer users' potential purchase intent from their limited queries and capture query relevant product features. We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval in both controlled and real-world experiments. Our ablation studies confirm that the proposed transformer architecture and integrated learning strategies enable the mining of key data sources to infer purchase intent, extract product features, and enhance the transformation pipeline from queries to more accurate pseudo-product representations.
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