A Fashion-Brand Recommender System Using Brand Association Rules and Features

Web services selling fashion clothes on Internet are rapidly increasing, so it is becoming difficult for users to find their favorite ones among the enormous number of fashion items available. Although several fashion brand recommender services are available to support the users to search clothes to be bought, the accuracy is so low that they need to check clothes one by one. In this paper, we propose a fashion-brand recommendation method based on both the fashion features and the fashion association rules. The fashion-brand association rules are used to select new brands for a user which are similar to user´s favorite ones. As the rules represent the frequent occurrences in fashion-brand liking, while the fashion-brand feature can be used to calculate similarities between brands. We also propose a new method which is a combination of these two. We combined these two methods into one in a serial-hybrid way. It is shown that a combined method produces the highest F-measure among other methods including existing services.