Multimodal semantic retrieval for product search

• URL: http://arxiv.org/abs/2501.07365v3
• Date: Mon, 17 Feb 2025 08:40:10 GMT
• Title: Multimodal semantic retrieval for product search
• Authors: Dong Liu, Esther Lopez Ramos,
• Abstract summary: We build a multimodal representation for product items in e-commerce search in contrast to pure-text representation of products. We demonstrate that a multimodal representation scheme for a product can show improvement on purchase recall or relevance accuracy in semantic retrieval.
• Score: 6.185573921868495
• License:
• Abstract: Semantic retrieval (also known as dense retrieval) based on textual data has been extensively studied for both web search and product search application fields, where the relevance of a query and a potential target document is computed by their dense vector representation comparison. Product image is crucial for e-commerce search interactions and is a key factor for customers at product explorations. However, its impact on semantic retrieval has not been well studied yet. In this research, we build a multimodal representation for product items in e-commerce search in contrast to pure-text representation of products, and investigate the impact of such representations. The models are developed and evaluated on e-commerce datasets. We demonstrate that a multimodal representation scheme for a product can show improvement either on purchase recall or relevance accuracy in semantic retrieval. Additionally, we provide numerical analysis for exclusive matches retrieved by a multimodal semantic retrieval model versus a text-only semantic retrieval model, to demonstrate the validation of multimodal solutions.

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