Abstract: As the scale of e-commerce continues to expand, the number and variety of products are rapidly increasing, requiring customers to spend a considerable amount of time to find the products they need. This process of browsing through large amounts of irrelevant information and products undoubtedly leads consumers to be drown in an overload of information. To address this issue, recommendation systems have emerged. Recommendation systems are advanced business intelligence platforms built on massive data mining foundations, designed to provide e-commerce websites with personalized decision support and information services tailored to their customers. The emergence and development of the Internet have triggered a digital storm, gradually applying recommendation technology to various fields such as e-commerce, news delivery, social networking, and music entertainment. Clothing, as an important component of the fashion industry, benefits from the integration of the Internet and the fashion industry, bringing new possibilities for clothing design, production, and consumption. Clothing recommendation, as a significant research direction in the computer fashion field, has garnered widespread attention from fields like computer vision, multimedia, and information retrieval. Compared to traditional offline shopping, online purchase of clothing and accessories is more convenient. A typical recommendation system predicts user interest in a particular item based on given information about the product and the user, as well as interaction history, thereby providing personalized products or services to the user. Clothing recommendation can be seen as a specific application of recommendation systems in the field of e-commerce, but it possesses uniqueness in many aspects. People's demands for personalized clothing quality, styles, and matching are constantly growing, making digital transformation crucial for the clothing industry. Faced with massive clothing data, clothing recommendation systems play a crucial role as a key link, including personalized recommendations, enhancing user experience, and increasing revenue, bringing numerous practical benefits to both users and businesses, and simultaneously driving the industry towards intelligent and efficient development. This article combines the key aspects of clothing recommendation systems and summarizes the general process and related technologies for creating clothing recommendation systems, including data collection and preprocessing, feature engineering, and model construction. It provides a detailed overview of key technologies in both traditional recommendation techniques and deep learning applied in the field of clothing recommendations, analyzing the application and expansion of various algorithms. In terms of application, clothing recommendation systems are widely used in e-commerce platforms and clothing styling recommendation apps, offering users convenient shopping and styling suggestions. Finally, based on the application areas and development trends of clothing recommendation systems, it explores the pressing issues that clothing recommendation systems need to address and future innovative directions.

Key words: recommendation system, recommendation algorithm, clothing matching, clothing recommendation, algorithm research, deep learning

摘要： 服装推荐系统在服装行业的数字化转型中发挥着重要的作用，它可以提供服装的个性化推荐，提升用户体验，增加产品营业额，推动服装行业向智能化与高效化发展。文章结合服装推荐系统的重要环节，从数据收集与预处理、特征工程、模型构建3个版块归纳总结了创建服装推荐系统的一般流程与相关技术，并对传统推荐技术和深度学习技术两个方向的关键技术进行了详细综述，分析各类算法在服装推荐领域中的应用及拓展。在应用领域方面，服装推荐系统广泛应用于电子商务平台和服装搭配推荐应用等场景，为用户提供便捷的选购与穿搭建议。最后基于服装推荐系统的应用领域与发展趋势，探讨其亟需解决的问题以及未来的创新方向。

关键词: 推荐系统, 推荐算法, 服装搭配, 服装推荐, 算法研究, 深度学习