Over the past decade, significant advances have been made in the field of
image search for e-commerce applications. Traditional image-to-image retrieval
models, which focus solely on image details such as texture, tend to overlook
useful semantic information contained within the images. As a result, the
retrieved products might possess similar image details, but fail to fulfil the
user's search goals. Moreover, the use of image-to-image retrieval models for
products containing multiple images results in significant online product
feature storage overhead and complex mapping implementations. In this paper, we
report the design and deployment of the proposed Multi-modal Item Embedding
Model (MIEM) to address these limitations. It is capable of utilizing both
textual information and multiple images about a product to construct meaningful
product features. By leveraging semantic information from images, MIEM
effectively supplements the image search process, improving the overall
accuracy of retrieval results. MIEM has become an integral part of the Shopee
image search platform. Since its deployment in March 2023, it has achieved a
remarkable 9.90% increase in terms of clicks per user and a 4.23% boost in
terms of orders per user for the image search feature on the Shopee e-commerce
platform.

在过去的十年中，人们在电子商务应用的图像搜索领域取得了重大进展。本文报告了多模式物品嵌入模型（MIEM）的设计和部署，以解决传统图像检索模型在商品多图像处理上的限制和存储负担，从而提高检索结果的准确性。在 Shopee 电子商务平台中部署后，MIEM 在图像搜索功能上实现了用户点击量的显著增长（9.90%）和用户订单量的提升（4.23%）。