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With the growth of online shopping for fashion products, accurate fashionrecommendation has become a critical problem. Meanwhile, social networksprovide an open and new data source for personalized fashion analysis. In thiswork, we study the problem of personalized fashion recommendation from socialmedia data, i.e. recommending new outfits to social media users that fit theirfashion preferences. To this end, we present an item-to-set metric learningframework that learns to compute the similarity between a set of historicalfashion items of a user to a new fashion item. To extract features frommulti-modal street-view fashion items, we propose an embedding module thatperforms multi-modality feature extraction and cross-modality gated fusion. Tovalidate the effectiveness of our approach, we collect a real-world socialmedia dataset. Extensive experiments on the collected dataset show the superiorperformance of our proposed approach.

Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach