  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.
