The booming advances of cloud computing promote rapid growth of the number of cloud service Application Program Interfaces (APIs) published at the large-scale software cloud markets. Cloud service APIs recommendation remains a challenging issue for a composite cloud system construction, due to massively available candidate component cloud services with similar (or identical) functionalities in the cloud markets. As for a specific user, the probability distribution of the data indicating his/her preferences to the cloud service APIs may change with time, resulting in concept drifting preferences. To adapt users’ preference drifts and provide effective recommendation results to composite cloud system developers, we propose a concept drift-aware temporal cloud service APIs recommendation approach for composite cloud systems (or CD-APIR) in this paper. First, we track users temporal preferences through users’ behavior-aware information analysis. Second, we utilize Singular Value Decomposition (SVD) method to predict the missing values in the user–service matrices. Third, we identify the degree of users preference drifts by Jensen–Shannon (or JS) divergence. Finally, we recommend cloud service APIs by presenting a piecewise trading-off equation. Experimental evaluations conducted on WS-Dream dataset demonstrate that the CD-APIR approach can effectively improve the accuracy of cloud service APIs recommendation comparing with 7 representative approaches.

云计算的蓬勃发展推动了在大型软件云市场上发布的云服务应用程序接口（API）数量的快速增长。由于在云市场中具有类似（或相同）功能的大量可用候选组件云服务，因此对于复合云系统构建，云服务API的建议仍然是一个具有挑战性的问题。对于特定用户，指示其对云服务API的偏好的数据的概率分布可能会随时间变化，从而导致概念漂移偏好。为了适应用户的偏爱漂移并向复合云系统开发人员提供有效的推荐结果，我们在本文中提出了一种针对复合云系统（或CD-APIR）的概念感知漂移的临时云服务API推荐方法。首先，我们通过用户的行为感知信息分析来跟踪用户的时间偏好。其次，我们利用奇异值分解（SVD）方法来预测用户服务矩阵中的缺失值。第三，我们通过Jensen-Shannon（或JS）差异来确定用户偏好漂移的程度。最后，我们通过提出分段权衡方程来推荐云服务API。在WS-Dream数据集上进行的实验评估表明，与7种代表性方法相比，CD-APIR方法可以有效地提高云服务API建议的准确性。我们通过Jensen-Shannon（或JS）差异来确定用户偏好漂移的程度。最后，我们通过提出分段权衡方程来推荐云服务API。在WS-Dream数据集上进行的实验评估表明，与7种代表性方法相比，CD-APIR方法可以有效提高云服务API建议的准确性。我们通过Jensen-Shannon（或JS）差异来确定用户偏好漂移的程度。最后，我们通过提出分段权衡方程来推荐云服务API。在WS-Dream数据集上进行的实验评估表明，与7种代表性方法相比，CD-APIR方法可以有效地提高云服务API建议的准确性。