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Learning Binary Code for Personalized Fashion Recommendation

With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great needs. Recommending fashion outfits, each of which is composed of multiple interacted clothing and accessories, is relatively new to the field. The problem becomes even more interesting and challenging when considering users' personalized fashion style. Another challenge in a large-scale fashion outfit recommendation system is the efficiency issue of item/outfit search and storage. In this paper, we propose to learn binary code for efficient personalized fashion outfits recommendation. Our system consists of three components, a feature network for content extraction, a set of type-dependent hashing modules to learn binary codes, and a matching block that conducts pairwise matching. The whole framework is trained in an end-to-end manner. We collect outfit data together with user label information from a fashion-focused social website for the personalized recommendation task. Extensive experiments on our datasets show that the proposed framework outperforms the state-of-the-art methods significantly even with a simple backbone.

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