Persona is one of the latest fashion magazines in Tokyo. It's printed on heavy stock paper and is full of photos of models and clothing. The only thing missing is text. An app recognizes the images, queries a cloud database and then downloads related information such as pricing and availability of dresses. Other photos feature images of tomatoes and wine, triggering a related vegetable delivery service and wine retailer, as well as online coupons.

Fashion magazines contain a number of photographs of fashion models, and their clothing coordinates serve as useful references. In this paper, we propose a recommender system for clothing coordinates using full-body photographs from fashion magazines. The task is that, given a photograph of a fashion item (e.g. tops) as a query, to recommend a photograph of other fashion items (e.g. bottoms) that is appropriate to the query. With the proposed method, we use a probabilistic topic model for learning information about coordinates from visual features in each fashion item region. We demonstrate the effectiveness of the proposed method using real photographs from a fashion magazine and two fashion style sharing services with the task of making top (bottom) recommendations given bottom (top) photographs.