The proliferation of available Web services presents a big challenge in selecting suitable services. Various methods have been devised to predict Quality of Service (QoS) values, aiming to address the service selection problem. However, these methods encounter numerous limitations that hinder their prediction accuracy. A key issue stems from the dynamic nature of the service environment, leading to fluctuations in QoS values due to factors like network load and hardware issues. To mitigate these challenges, QoS selection methods have leveraged contextual information from the surrounding environments, such as service invocation time, user, and service locations. Among these methods, Collaborative Filtering (CF) has gained notable importance. In recent years, several CF methods have incorporated service invocation time into their prediction processes, giving rise to what is commonly known as time-aware CF methods. Despite the increasing adoption of time-aware CF methods, there remains a notable absence of a dedicated and comprehensive literature review on this topic. Addressing this gap, this paper conducts an analysis of the literature, reviewing the forty (40) most prominent studies in this domain. It offers a thematic categorization of these studies along with an insightful analysis outlining their objectives, advantages, and limitations. The review also identifies key research gaps and proposes potential directions for future investigations. Overall, this literature review serves as an up-to-date resource for researchers engaged in service-oriented computing research.

可用 Web 服务的激增给选择合适的服务带来了巨大的挑战。人们已经设计了各种方法来预测服务质量（QoS）值，旨在解决服务选择问题。然而，这些方法遇到了许多限制，影响了它们的预测准确性。一个关键问题源于服务环境的动态特性，由于网络负载和硬件问题等因素，导致 QoS 值波动。为了缓解这些挑战，QoS 选择方法利用了周围环境的上下文信息，例如服务调用时间、用户和服务位置。在这些方法中，协同过滤（CF）变得尤为重要。近年来，一些 CF 方法已将服务调用时间纳入其预测过程，从而产生了通常所说的时间感知 CF 方法。尽管越来越多地采用时间感知的 CF 方法，但仍然明显缺乏关于该主题的专门且全面的文献综述。为了解决这一差距，本文对文献进行了分析，回顾了该领域四十 (40) 项最著名的研究。它对这些研究进行了主题分类，并进行了富有洞察力的分析，概述了它们的目标、优点和局限性。该审查还确定了关键的研究差距，并提出了未来研究的潜在方向。总体而言，这篇文献综述为从事面向服务的计算研究的研究人员提供了最新的资源。