  A large number of empirical studies on applying self-attention models in the
domain of recommender systems are based on offline evaluation and metrics
computed on standardized datasets. Moreover, many of them do not consider side
information such as item and customer metadata although deep-learning
recommenders live up to their full potential only when numerous features of
heterogeneous type are included. Also, normally the model is used only for a
single use case. Due to these shortcomings, even if relevant, previous works
are not always representative of their actual effectiveness in real-world
industry applications. In this talk, we contribute to bridging this gap by
presenting live experimental results demonstrating improvements in user
retention of up to 30\%. Moreover, we share our learnings and challenges from
building a re-usable and configurable recommender system for various
applications from the fashion industry. In particular, we focus on fashion
inspiration use-cases, such as outfit ranking, outfit recommendation and
real-time personalized outfit generation.
