  Over the past years, fashion-related challenges have gained a lot of
attention in the research community. Outfit generation and recommendation,
i.e., the composition of a set of items of different types (e.g., tops, bottom,
shoes, accessories) that go well together, are among the most challenging ones.
That is because items have to be both compatible amongst each other and also
personalized to match the taste of the customer. Recently there has been a
plethora of work targeted at tackling these problems by adopting various
techniques and algorithms from the machine learning literature. However, to
date, there is no extensive comparison of the performance of the different
algorithms for outfit generation and recommendation. In this paper, we close
this gap by providing a broad evaluation and comparison of various algorithms,
including both personalized and non-personalized approaches, using online,
real-world user data from one of Europe's largest fashion stores. We present
the adaptations we made to some of those models to make them suitable for
personalized outfit generation. Moreover, we provide insights for models that
have not yet been evaluated on this task, specifically, GPT, BERT and
Seq-to-Seq LSTM.
