Research Progress and Prospects of Service Recommendation Methods (Invited)

With the rapid development of the Internet, cloud computing, and artificial intelligence, service recommendation has become a key technique in service computing. It helps users find appropriate services quickly and accurately, improves resource utilization, and enhances user experience. This paper presents a systematic review of the research progress in service recommendation and summarizes representative studies. This review introduces three main recommendation methods: traditional method, context-aware, and neural network-based. Each category is described in terms of fundamental principles, typical applications, advantages, and limitations. This paper also discusses the major challenges in service recommendation, including data sparsity and cold start; incomplete and noisy Quality of Service (QoS) data; dynamic changes in services and contexts; insufficient explainability; and issues of real-time performance, scalability, privacy, and security. Finally, this paper presents an overview of the limitations of current research and explores future research directions. Emerging technologies, such as big data analytics, Knowledge Graphs (KGs), deep learning, Large Language Models (LLMs), and reinforcement learning, have been highlighted as promising approaches for improving the intelligence, personalization, and trustworthiness of service recommendations. This review provides a comprehensive understanding of the field and serves as a valuable reference for further research and practical applications.