服务推荐方法的研究进展与展望(特邀)

doi:10.19678/j.issn.1000-3428.0252977

计算机工程 ›› 2026, Vol. 52 ›› Issue (1): 61-75. doi: 10.19678/j.issn.1000-3428.0252977

• 大模型时代的服务计算 • 上一篇下一篇

服务推荐方法的研究进展与展望(特邀)

赵旭东¹, 吴洪越², 孟柯¹, 许小龙¹^,*(), 窦万春³

1. 南京信息工程大学软件学院, 江苏南京 210044
2. 天津大学智能与计算学部, 天津 300350
3. 南京大学计算机软件新技术国家重点实验室, 江苏南京 210023

收稿日期:2025-09-01 修回日期:2025-12-08 出版日期:2026-01-15 发布日期:2026-01-15
通讯作者: 许小龙
作者简介:
赵旭东, 男, 硕士研究生, 主研方向为推荐系统、服务推荐
吴洪越, 副教授、博士
孟柯, 博士研究生
许小龙(通信作者), 教授、博士
窦万春, 教授、博士
基金资助:
国家自然科学基金重大研究计划子课题(92267104); 江苏省前沿引领技术基础研究重大专项(BK20232032)

Research Progress and Prospects of Service Recommendation Methods (Invited)

ZHAO Xudong¹, WU Hongyue², MENG Ke¹, XU Xiaolong¹^,*(), DOU Wanchun³

1. School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
2. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, Jiangsu, China

Received:2025-09-01 Revised:2025-12-08 Online:2026-01-15 Published:2026-01-15
Contact: XU Xiaolong

摘要/Abstract

摘要：

随着互联网、云计算和人工智能的不断发展, 服务推荐作为服务计算中的关键技术, 在帮助用户快速精准地发现目标服务、提升资源利用率和改善用户体验方面发挥着越来越重要的作用。针对服务推荐的研究问题与发展趋势, 对现有研究成果进行了系统梳理和全面概述。首先, 总结了服务推荐的研究现状, 包括基于传统方法的服务推荐、基于上下文感知的服务推荐以及基于神经网络的服务推荐, 系统分析了各类方法的基本原理、代表性工作及其优缺点, 并对比了它们在不同应用场景中的适用性与表现。其次, 深入探讨了服务推荐在实际应用中所面临的核心挑战, 涵盖数据稀疏与冷启动、服务质量(QoS)数据不完整与含噪声、服务动态性与上下文变化、推荐结果的可解释性, 以及系统的实时性、可扩展性、隐私与安全等问题。最后, 对服务推荐技术进行回顾与总结, 概述了当前研究中的局限性与主要问题, 并结合大数据、知识图谱(KG)、深度学习、大语言模型(LLM)与强化学习等新兴技术的发展, 探讨了服务推荐未来的发展方向与研究前景。本研究有助于加深对服务推荐领域的整体理解, 并为后续研究和应用实践提供参考。

关键词: 服务推荐, 服务质量, 上下文感知, 深度学习, 数据挖掘

Abstract:

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.

Key words: service recommendation, Quality of Service (QoS), context-aware, deep learning, data mining

赵旭东, 吴洪越, 孟柯, 许小龙, 窦万春. 服务推荐方法的研究进展与展望(特邀)[J]. 计算机工程, 2026, 52(1): 61-75.

ZHAO Xudong, WU Hongyue, MENG Ke, XU Xiaolong, DOU Wanchun. Research Progress and Prospects of Service Recommendation Methods (Invited)[J]. Computer Engineering, 2026, 52(1): 61-75.

https://www.ecice06.com/CN/Y2026/V52/I1/61

图/表 8

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