Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large language models (LLMs) offers a new horizon for redefining recommender systems with vast general knowledge and reasoning capabilities. Standing across this LLM era, we aim to integrate recommender systems into a broader picture, and pave the way for more comprehensive solutions for future research. Therefore, we first offer a comprehensive overview of the technical progression of recommender systems, particularly focusing on language foundation models and their applications in recommendation. We identify two evolution paths of modern recommender systems -- via list-wise recommendation and conversational recommendation. These two paths finally converge at LLM agents with superior capabilities of long-term memory, reflection, and tool intelligence. Along these two paths, we point out that the information effectiveness of the recommendation is increased, while the user's acquisition cost is decreased. Technical features, research methodologies, and inherent challenges for each milestone along the path are carefully investigated -- from traditional list-wise recommendation to LLM-enhanced recommendation to recommendation with LLM agents. Finally, we highlight several unresolved challenges crucial for the development of future personalization technologies and interfaces and discuss the future prospects.

翻译：推荐系统（RS）对于管理信息过载和提供个性化内容至关重要，能够响应用户多样化的信息需求。大型语言模型（LLM）的出现为利用其广泛通用知识和推理能力重新定义推荐系统开辟了新视野。立足于这一LLM时代，我们旨在将推荐系统纳入更广阔的图景，并为未来研究提供更全面的解决方案铺平道路。为此，我们首先全面概述了推荐系统的技术演进，特别聚焦于语言基础模型及其在推荐中的应用。我们识别出现代推荐系统的两条演化路径——通过列表式推荐和对话式推荐。这两条路径最终汇聚于具备长期记忆、反思和工具智能等卓越能力的LLM智能体。沿着这两条路径，我们指出推荐的信息有效性得到提升，同时用户获取成本得以降低。我们对路径上每个里程碑的技术特征、研究方法和固有挑战进行了细致考察——从传统的列表式推荐到LLM增强推荐，再到基于LLM智能体的推荐。最后，我们强调了若干对未来个性化技术和界面发展至关重要的未解挑战，并探讨了未来前景。