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arXiv · 2301.12646

MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search

Xiaoyang Zheng·Zilong Wang·Ke Xu·Sen Li·Tao Zhuang·Qingwen Liu·Xiaoyi Zeng
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Abstract

Taobao Search consists of two phases: the retrieval phase and the ranking phase. Given a user query, the retrieval phase returns a subset of candidate products for the following ranking phase. Recently, the paradigm of pre-training and fine-tuning has shown its potential in incorporating visual clues into retrieval tasks. In this paper, we focus on solving the problem of text-to-multimodal retrieval in Taobao Search. We consider that users' attention on titles or images varies on products. Hence, we propose a novel Modal Adaptation module for cross-modal fusion, which helps assigns appropriate weights on texts and images across products. Furthermore, in e-commerce search, user queries tend to be brief and thus lead to significant semantic imbalance between user queries and product titles. Therefore, we design a separate text encoder and a Keyword Enhancement mechanism to enrich the query representations and improve text-to-multimodal matching. To this end, we present a novel vision-language (V+L) pre-training methods to exploit the multimodal information of (user query, product title, product image). Extensive experiments demonstrate that our retrieval-specific pre-training model (referred to as MAKE) outperforms existing V+L pre-training methods on the text-to-multimodal retrieval task. MAKE has been deployed online and brings major improvements on the retrieval system of Taobao Search.

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Xiaoyang Zheng, Zilong Wang, Ke Xu, Sen Li, Tao Zhuang, Qingwen Liu, Xiaoyi Zeng. 2023-01-30. MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search. https://doi.org/10.1145/3543873.3584627

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