Multimodal semantic retrieval for product search
This addresses the need for better product search in e-commerce by leveraging multimodal data, though it appears incremental as it builds on existing semantic retrieval techniques.
The paper tackled the problem of incorporating product images into semantic retrieval for e-commerce search, demonstrating that multimodal representations improve purchase recall or relevance accuracy compared to text-only methods.
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.