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Increasing demand for fashion recommendation raises a lot of challenges foronline shopping platforms and fashion communities. In particular, there existtwo requirements for fashion outfit recommendation: the Compatibility of thegenerated fashion outfits, and the Personalization in the recommendationprocess. In this paper, we demonstrate these two requirements can be satisfiedvia building a bridge between outfit generation and recommendation. Throughlarge data analysis, we observe that people have similar tastes in individualitems and outfits. Therefore, we propose a Personalized Outfit Generation (POG)model, which connects user preferences regarding individual items and outfitswith Transformer architecture. Extensive offline and online experiments providestrong quantitative evidence that our method outperforms alternative methodsregarding both compatibility and personalization metrics. Furthermore, wedeploy POG on a platform named Dida in Alibaba to generate personalized outfitsfor the users of the online application iFashion.  This work represents a first step towards an industrial-scale fashion outfitgeneration and recommendation solution, which goes beyond generating outfitsbased on explicit queries, or merely recommending from existing outfit pools.As part of this work, we release a large-scale dataset consisting of 1.01million outfits with rich context information, and 0.28 billion user clickactions from 3.57 million users. To the best of our knowledge, this dataset isthe largest, publicly available, fashion related dataset, and the first toprovide user behaviors relating to both outfits and fashion items.

POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion