Relevance module plays a fundamental role in e-commerce search as they are responsible for selecting relevant products from thousands of items based on user queries, thereby enhancing users experience and efficiency. The traditional approach models the relevance based product titles and queries, but the information in titles alone maybe insufficient to describe the products completely. A more general optimization approach is to further leverage product image information. In recent years, vision-language pre-training models have achieved impressive results in many scenarios, which leverage contrastive learning to map both textual and visual features into a joint embedding space. In e-commerce, a common practice is to fine-tune on the pre-trained model based on e-commerce data. However, the performance is sub-optimal because the vision-language pre-training models lack of alignment specifically designed for queries. In this paper, we propose a method called Query-LIFE (Query-aware Language Image Fusion Embedding) to address these challenges. Query-LIFE utilizes a query-based multimodal fusion to effectively incorporate the image and title based on the product types. Additionally, it employs query-aware modal alignment to enhance the accuracy of the comprehensive representation of products. Furthermore, we design GenFilt, which utilizes the generation capability of large models to filter out false negative samples and further improve the overall performance of the contrastive learning task in the model. Experiments have demonstrated that Query-LIFE outperforms existing baselines. We have conducted ablation studies and human evaluations to validate the effectiveness of each module within Query-LIFE. Moreover, Query-LIFE has been deployed on Miravia Search, resulting in improved both relevance and conversion efficiency.

翻译：相关性模块在电商搜索中扮演基础性角色，其通过用户查询从海量商品中筛选相关商品，从而提升用户体验与效率。传统方法基于商品标题与查询建模相关性，但标题信息可能不足以完整描述商品。更通用的优化方法是进一步利用商品图像信息。近年来，视觉-语言预训练模型通过对比学习将文本与视觉特征映射到联合嵌入空间，已在诸多场景取得显著成果。在电商领域，常见做法是基于电商数据对预训练模型进行微调。然而，由于视觉-语言预训练模型缺乏专为查询设计的对齐机制，其性能并非最优。本文提出Query-LIFE（面向查询的语言-图像融合嵌入）方法以应对这些挑战。Query-LIFE利用基于查询的多模态融合技术，根据商品类型有效整合图像与标题信息。此外，该方法采用查询感知模态对齐增强商品综合表征的准确性。进一步地，我们设计了GenFilt模块，利用大模型的生成能力过滤假负样本，从而提升模型中对比学习任务的整体性能。实验表明，Query-LIFE优于现有基线方法。我们通过消融实验与人工评估验证了Query-LIFE各模块的有效性。此外，Query-LIFE已在Miravia搜索平台部署，显著提升了相关性与转化效率。