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：纺织服装行业在过去几年中取得了巨大发展。顾客无需再逛多家实体店、排长队或试衣间试穿，因为数百万种商品已出现在线上目录中。然而，面对海量可选商品，需要有效的推荐系统来恰当分类、排序并向用户传达相关产品信息。高效的时尚推荐系统能显著影响数十亿顾客的购物体验，同时提升供应商侧的销售额与收入。本综述旨在梳理专门针对服装时尚垂直领域的推荐系统研究。我们识别了时尚推荐系统研究中最紧迫的挑战，并构建了分类体系：根据文献试图实现的目标（如单品推荐、套装推荐、尺码推荐、可解释性等）以及辅助信息类型（用户、物品、上下文）进行分类。我们还明确了最重要的评估目标与视角（套装生成、套装推荐、配对推荐、填空式套装兼容性预测）以及最常用的数据集与评估指标。