Content-based fabric recommendation system integrating image and text information

Abstract

The increasing popularity of e-commerce and online fashion platforms has led to a growing demand for effective fabric recommendation systems. Choosing the suitable fabric is crucial to achieving desired aesthetic outcomes, ensuring product quality, and enhancing user satisfaction in the fashion design and textile manufacturing industries. Although our research group has made significant progress in fabric retrieval, we have encountered several challenges in practical collaborations with industry partners. These challenges include users’ difficulty expressing their preferences and the lack of personalization in fabric retrieval. We proposed a content-based fabric recommendation system that combines image and text information to address these challenges. Our approach employed various methods to extract hand-crafted features, such as image color and texture, to characterize the visual communication of fabrics. We combined this with text information to provide a more comprehensive fabric representation. By mining user preferences and employing personalized recommendation algorithms, our system delivered personalized fabric recommendations to cater to individual needs. Experimentation with comparison texts, colors, and textures separately or together. According to the assessment results, the highest F1 value of 83% can be achieved when the color, texture, and text combine with the color: texture: text = 1:1:1 and the filter value = 2.2. Precision and recall rates are also 83% at this point. Our proposed fabric recommendation system is useful and efficient.