PAN: Personalized Attention Network For Outfit Recommendation

Recent years have witnessed the dramatic development of e-fashion industry, it becomes essential to build an intelligent fashion recommender system. Most of existing works on fashion recommendation focus on modeling the general compatibility while ignoring the user preferences. In this paper, we present a Personalized Attention Network (PAN) for fashion recommendation. The key component of PAN includes a user encoder, an item encoder and a preference predictor. To modeling users’ diverse interests, we develop an attention network to incorporate the learnt user representation into the item encoder component. More specifically, the attention module consists of a sequential user-aware channel-level and a spatial-level sub-module. Moreover, a novel ranking an user-specific loss, is proposed to capture the interest of different users on the same outfit. To make the training more effective and efficient, a novel user-aware online hard negative mining strategy is proposed. Extensive experiments on Polyvore-U dataset demonstrate the excellence of the proposed system and the effectiveness of different modules.