基于社会信任的云服务推荐:综述

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Concurrency and Computation-Practice & Experience Pub Date : 2024-08-20 DOI:10.1002/cpe.8262
Fatma Zohra Lebib, Saida Kichou
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引用次数: 0

摘要

企业的持续扩张和发展需要强大的计算能力和海量数据存储系统。云服务以简单、灵活和安全的方式提供这些资源。目前,类似的云服务种类繁多,功能各异,这就需要一个推荐系统。基于服务质量(QoS)的推荐是第一代服务推荐系统,它只考虑所有用户的评级信息,不加区分。然而,这些系统存在许多缺陷,如冷启动和数据稀疏问题,以及推荐结果的准确性和可靠性较差。为了解决这些问题并提高推荐质量,新一代推荐系统应运而生,如情境感知推荐系统、特定领域推荐系统和信任感知推荐系统。目前,这些系统更加关注如何利用用户在社交网络中相互交流产生的社交数据,针对用户需求推荐更合适、更可靠的服务。考虑到信任在云环境中的重要性,本研究旨在概述迄今为止提出的基于信任的云服务推荐方法的研究情况,并强调当前使用深度学习等新技术应对某些挑战的趋势。
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Recommending cloud services based on social trust: An overview

The continued expansion and development of the business requires great computing power and massive data storage systems. Cloud services deliver these resources in a simple, flexible and secure way. There is now a wide range of similar cloud services with different capabilities, which requires a recommendation system. Recommendation based on Quality of Service (QoS) is the first generation of service recommendation systems that only takes into account the rating information of all users without distinction. However, these systems suffer from many shortcomings, such as cold start and data sparsity issues, as well as poor accuracy and reliability of recommendation results. To address these issues and improve the quality of recommendations, a new generation of recommender systems has emerged, such as context-aware, domain-specific, and trust-aware recommender systems. These systems now focus more on how to leverage social data generated from user interactions with each other in social networks to recommend more suitable and reliable services in response to user needs. Due to the importance of considering trust in cloud environments, this study aims to provide an overview of the research on trust-based cloud service recommendation approaches proposed so far and highlights the current trend towards use new technologies such as deep learning to deal with certain challenges.

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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
期刊最新文献
Issue Information Improving QoS in cloud resources scheduling using dynamic clustering algorithm and SM-CDC scheduling model Issue Information Issue Information Camellia oleifera trunks detection and identification based on improved YOLOv7
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