A Multimodal Retrieval-Based Framework for Popularity Prediction of Shein T-Shirts

Popularity analysis of fashion clothing items is important, as it provides valuable insights for fashion product design and market strategy development, ultimately fostering innovation and driving economic growth. Existing approaches primarily employ computer vision techniques to extract features from clean clothing product images for predicting market response, overlooking other influential factors that contribute to a product's popularity. Moreover, since they depend on high-dimensional and abstract visual features to predict item popularity, they are prone to overfitting and offer limited explainability. To address these challenges, we propose a multimodal retrieval-based framework for predicting clothing popularity, with a case study on fast-fashion SHEIN T-shirts. To be specific, we first design a set of fashion-related questions, which are used as prompts for ChatGPT to generate structured textual descriptions for each product. These descriptions, along with the corresponding product images, serve as inputs to a multimodal retrieval model, which returns the top-k most similar popular and unpopular items for each query product. Instead of using raw high-dimensional embeddings, we calculate similarity scores between the query product and the retrieved top-k popular and unpopular items. In addition to these similarity scores, we incorporate numerical features (e.g., price, discount level) and categorical features (e.g., color etc) to build a multi-factor popularity prediction model. Experimental results demonstrate the effectiveness of the proposed framework for popularity prediction of SHEIN T-shirts and indicate the relative importance of multiple factors in shaping product popularity.