  The textile and apparel industries have grown tremendously over the last few
years. Customers no longer have to visit many stores, stand in long queues, or
try on garments in dressing rooms as millions of products are now available in
online catalogs. However, given the plethora of options available, an effective
recommendation system is necessary to properly sort, order, and communicate
relevant product material or information to users. Effective fashion RS can
have a noticeable impact on billions of customers' shopping experiences and
increase sales and revenues on the provider side. The goal of this survey is to
provide a review of recommender systems that operate in the specific vertical
domain of garment and fashion products. We have identified the most pressing
challenges in fashion RS research and created a taxonomy that categorizes the
literature according to the objective they are trying to accomplish (e.g., item
or outfit recommendation, size recommendation, explainability, among others)
and type of side-information (users, items, context). We have also identified
the most important evaluation goals and perspectives (outfit generation, outfit
recommendation, pairing recommendation, and fill-in-the-blank outfit
compatibility prediction) and the most commonly used datasets and evaluation
metrics.
