Product Recommendation Method based on Onomatopoeia Expressing Texture

With the widespread use of online shopping in recent years, consumer search requests for products have become more diverse. Previous web search methods have used adjectives as input by consumers. However, given that the number of adjectives that can be used to express textures is limited, it is debatable whether adjectives are capable of richly expressing variations of product textures. In Japanese, tactile experiences are easily and frequently expressed by onomatopoeia, such as “fuwa-fuwa” which indicates a soft and light sensation. Onomatopoeia are useful for understanding not only material textures but also a user’s intuitive, sensitive, and even ambiguous feelings evoked by materials. In this study, we propose a system to recommend products corresponding to product textures associated with Japanese onomatopoeia based on their symbolic sound associations between the onomatopoeia phonemes and the texture sensations. Our system quantitatively estimates the texture sensations of onomatopoeia input by users, and calculates the similarities between the users’ impressions of the onomatopoeia and those of product pictures. Our system also suggests products which best match the entered onomatopoeia. An evaluation of our method revealed that the best performance was achieved when the SIFT features, the colors of product pictures, and text describing product pictures were used; Specifically, precision was 66 for the top 15 search results. Our system is expected to contribute to online shopping activity as an intuitive product recommendation system.