Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation, have been posed and explored in the literature. The continuing research attention and advances impel us to look back and in-depth into the field for a better understanding. In this paper, we comprehensively review recent research efforts on fashion recommendation from a technological perspective. We first introduce fashion recommendation at a macro level and analyse its characteristics and differences with general recommendation tasks. We then clearly categorize different fashion recommendation efforts into several sub-tasks and focus on each sub-task in terms of its problem formulation, research focus, state-of-the-art methods, and limitations. We also summarize the datasets proposed in the literature for use in fashion recommendation studies to give readers a brief illustration. Finally, we discuss several promising directions for future research in this field. Overall, this survey systematically reviews the development of fashion recommendation research. It also discusses the current limitations and gaps between academic research and the real needs of the fashion industry. In the process, we offer a deep insight into how the fashion industry could benefit from fashion recommendation technologies. the computational technologies of fashion recommendation.

翻译：时尚推荐是计算时尚研究中的关键领域，近年来在计算机视觉、多媒体和信息检索领域引起了广泛关注。由于应用需求巨大，文献中提出了多种时尚推荐任务，如个性化时尚产品推荐、互补（搭配）推荐和整体造型推荐等。持续的研究关注和进展促使我们回顾并深入理解这一领域。本文从技术角度全面综述了近年来关于时尚推荐的研究工作。我们首先在宏观层面介绍时尚推荐，分析其特征以及与通用推荐任务的差异。然后，将不同的时尚推荐研究明确划分为若干子任务，并针对每个子任务，从问题定义、研究重点、现有最优方法和局限性等方面进行深入讨论。此外，我们总结了文献中用于时尚推荐研究的数据集，以便为读者提供简要说明。最后，讨论了该领域未来几个有前景的研究方向。总体而言，本综述系统梳理了时尚推荐研究的发展历程，讨论了当前存在的局限性以及学术研究与时尚产业实际需求之间的差距，并深入分析了时尚推荐技术如何为时尚产业带来价值。