  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, without insights on how these models perform
in real life scenarios. Moreover, many of them do not consider information such
as item and customer metadata, although deep-learning recommenders live up to
their full potential only when numerous features of heterogeneous types are
included. Also, typically recommendation models are designed to serve well only
a single use case, which increases modeling complexity and maintenance costs,
and may lead to inconsistent customer experience. In this work, we present a
reusable Attention-based Fashion Recommendation Algorithm (AFRA), that utilizes
various interaction types with different fashion entities such as items (e.g.,
shirt), outfits and influencers, and their heterogeneous features. Moreover, we
leverage temporal and contextual information to address both short and
long-term customer preferences. We show its effectiveness on outfit
recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit
recommendations by style; 3) similar item recommendation and 4) in-session
recommendations inspired by most recent customer actions. We present both
offline and online experimental results demonstrating substantial improvements
in customer retention and engagement.
