The textile and apparel industries have grown tremendously over the last 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:过去几年来,纺织业和服装业有了巨大的发展,客户不再需要访问许多商店、长队列或试穿更衣室服装,因为现在在线目录中已有数以百万计的产品,然而,鉴于现有的选择太多,有效的推荐系统对于适当分类、订购和向用户传递相关的产品材料或信息十分必要。有效时尚的RS可以对数十亿客户的购物经验产生显著影响,增加供应商的销售和收入。这次调查的目的是审查在服装和时装产品特定垂直领域运作的推荐系统。我们确定了时装学研究中最紧迫的挑战,并创建了分类学,根据它们努力完成的目标(例如项目或结构建议、规模建议、解释性建议等)和副信息类型(用户、项目、背景)将文献分类。我们还确定了最重要的评价目标和观点(适合生成、配对建议、配对建议和填充版装品的兼容性预测)以及最常用的数据设置和衡量标准。