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.

翻译：纺织与服装产业在过去几年经历了迅猛增长。随着数百万产品如今可通过在线目录获取，顾客无需再穿梭于多家门店、排长队或在试衣间试穿衣物。然而，面对海量的选择，一个有效的推荐系统对于恰当分类、排序并向用户传递相关产品信息至关重要。高效的时尚推荐系统能够显著影响数十亿顾客的购物体验，同时为供应商提升销售额和收入。本综述旨在对专门针对服装与时尚产品这一垂直领域的推荐系统进行回顾。我们识别出时尚推荐系统研究中最紧迫的挑战，并根据文献试图达成的目标（如单品或套装推荐、尺码推荐、可解释性等）以及辅助信息的类型（用户、物品、情境）创建了一个分类体系。我们还确定了最重要的评估目标与视角（套装生成、套装推荐、配对推荐及填空式套装兼容性预测），以及最常用的数据集和评估指标。