Web Service QoS Prediction via Collaborative Filtering: A Survey
Zibin Zheng, Xiaoli Li, Mingdong Tang, Fenfang Xie, Michael R. Lyu
Abstract
With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.