  Fashion is a unique domain for developing recommender systems (RS).
Personalization is critical to fashion users. As a result, highly accurate
recommendations are not sufficient unless they are also specific to users.
Moreover, fashion data is characterized by a large majority of new users, so a
recommendation strategy that performs well only for users with prior
interaction history is a poor fit to the fashion problem. Critical to
addressing these issues in fashion recommendation is an evaluation strategy
that: 1) includes multiple metrics that are relevant to fashion, and 2) is
performed within segments of users with different interaction histories. Here,
we present our multifaceted offline strategy for evaluating fashion RS. Using
our proposed evaluation methodology, we compare the performance of three
different algorithms, a most popular (MP) items strategy, a collaborative
filtering (CF) strategy, and a content-based (CB) strategy. We demonstrate that
only by considering the performance of these algorithms across multiple metrics
and user segments can we determine the extent to which each algorithm is likely
to fulfill fashion users' needs.
