[3E6-GS-10] AI application:

[3E6-GS-10-02] An Experimental Study on Query-based Thumbnail Optimization for E-Commerce Search Results

〇Ryohei Izawa¹, Ippei Yoshimoto¹, Shuri Hirowatari¹, Ryotaro Shimizu², Yuki Saito², Takashi Wada² (1. ZOZO, Inc., 2. ZOZO Research)

Keywords:E-Commerce, Fashion, Multi-Armed Bandit, Thumbnail Image

Thumbnails on e-commerce search result pages play a crucial role in influencing user click behavior. However, there has been limited research on identifying which images within the same product are most effective at driving clicks and how to optimize their selection. In this study, we conduct experiments on a real-world fashion e-commerce platform to analyze whether different thumbnails lead to variations in click-through rate (CTR) and whether the most effective thumbnails differ based on search queries. In addition, we apply the Multi-Armed Bandit algorithms to the collected data to explore the potential for optimizing thumbnail selection strategies tailored to search queries. Our experiments reveal significant differences in CTR across thumbnails and demonstrate that the effective thumbnails vary depending on the gender associated with the search query. Furthermore, applying Thompson Sampling based on gender-specific CTR distributions improves reward performance compared to other methods.