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