Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Due to the diversification of internet platforms and the exponential growth of users and items, the importance of cold-start recommendation (CSR) is becoming increasingly evident. At the same time, large language models (LLMs) have achieved tremendous success and possess strong capabilities in modeling user and item information, providing new potential for cold-start recommendations. However, the research community on CSR still lacks a comprehensive review and reflection in this field. Based on this, in this paper, we stand in the context of the era of large language models and provide a comprehensive review and discussion on the roadmap, related literature, and future directions of CSR. Specifically, we have conducted an exploration of the development path of how existing CSR utilizes information, from content features, graph relations, and domain information, to the world knowledge possessed by large language models, aiming to provide new insights for both the research and industrial communities on CSR. Related resources of cold-start recommendations are collected and continuously updated for the community in https://github.com/YuanchenBei/Awesome-Cold-Start-Recommendation.

翻译：冷启动问题是推荐系统中长期存在的挑战之一，其核心在于准确建模新用户或交互有限的用户与物品，以提供更优质的推荐服务。随着互联网平台的多样化以及用户与物品数量的指数级增长，冷启动推荐的重要性日益凸显。与此同时，大语言模型取得了巨大成功，并在建模用户与物品信息方面展现出强大能力，为冷启动推荐带来了新的潜力。然而，冷启动推荐研究领域目前仍缺乏对这一方向的全面梳理与反思。基于此，本文立足于大语言模型时代背景，对冷启动推荐的发展路线、相关文献及未来方向进行了全面综述与探讨。具体而言，我们系统梳理了现有冷启动推荐方法在信息利用方面的发展路径——从内容特征、图关系、领域信息，到大语言模型所蕴含的世界知识，旨在为冷启动推荐的研究界与工业界提供新的见解。冷启动推荐的相关资源已收集并在 https://github.com/YuanchenBei/Awesome-Cold-Start-Recommendation 持续更新，以供学界参考。