Style Analysis of E-Commerce Site Images Using Multimodal Embeddings

Abstract

With the expansion of the e-commerce market and advancements in technology, a detailed analysis of consumer purchasing behavior and understanding of preferences have become crucial. This is particularly true where the visual appeal of product images plays a significant role in consumer engagement. In our study, we utilized multimodal embeddings to analyze the style and nuances of art images on e-commerce sites. Specifically, we employed COCA (Contrastive Captioners as Image-Text Foundation Models) to extract multimodal embeddings that capture the complex patterns and stylistic elements of product images. We then clustered these images into distinct style groups. Our analysis revealed that multimodal embeddings are effective in detecting subtle stylistic changes in images. Furthermore, it suggested that the application of such generative AI could greatly enhance the understanding of image characteristics preferred by consumers.