Ranking-Based Cloud Service Recommendation
Xianrong Zheng, Li Da Xu, Sheng Chai · 2017
As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of Service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. As a result, how to help users to find cloud services that meet their QoS requirements becomes an important problem. In this paper, we argue for cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud services. The approach can predict both QoS ratings and rankings for cloud services. To evaluate the effectiveness of the approach, we conduct extensive simulations. Results show that the approach can achieve more reliable rankings than one using the Pearson coefficient.