Collaborative QoS Prediction via Matrix Factorization and Topic Model
With the explosive growth of Web services on the Internet,Quality-of-Service-based (QoS) service selection is becoming animportant issue of service-oriented computing.The QoS values of services to current users are all supposed to known in the previous works, while lots of them are not known in reality.In order to predict the missing data, many approaches have been employed in recent years.However,those approaches dont carefully consider the online cold-start scenario where many new registered Web services havent been involved even once.This paper proposes a collaborative QoS prediction framework named CQP integrating matrix factorization with probabilistic topic model.This approach builds an integral latent user and Web service representative space, and can be applied online to predict QoS value and handle the online cold-start problem.To validate our methods, some approaches and our algorithm are conducted on a real-world dataset.The experiment result demonstrates that the proposed approach outperforms the previous works in prediction accuracy.
Web service QoS prediction collabrative filtering topic model
Tingting Liang Lichuan Ji Liang Chen Jian Wu Zhaohui Wu
College of computer science & technology,Zhejiang University,Hangzhou,310027
国际会议
昆明
英文
316-326
2014-05-01（万方平台首次上网日期，不代表论文的发表时间）