Fashion recommendations using text mining and multiple content attributes

Many online stores actively recommend commodities to users for facilitating easy product selection and increasing product exposure. Typical approach is by collaborative filtering, namely recommending the products based on their popularity, assuming that users may buy the products that many others have purchased. However, fashion recommendation is different from other product recommendations, because people may not like to go with the crowd in selecting fashion items. Other approaches of fashion recommendations include providing suggestions based on users’ purchase or browsing history. This is mainly done by searching similar products using commodities’ tags. Yet, the accuracy of tag-based recommendations may be limited due to ambiguous text expression and nonstandard tag names for fashion items. In this paper we collect a large fashion clothing dataset from different online stores. We develop a fashion keyword library by statistical natural language processing, and then we formulate an algorithm to automatically label fashion product attributes according to the defined library by text mining and semantic analysis. Lastly, we develop novel fashion recommendation models to select similar and mix-and-match products by integrating text-based product attributes and image extracted features. We evaluate the effectiveness of our approach by experiment over real datasets