A Data-Aware Latent Factor Model for Web Service QoS Prediction

摘要：

　　Accurately predicting unknown quality-of-service(QoS)data based on historical QoS records is vital in web service recommendation or selection.Recently,latent factor(LF)model has been widely and successfully applied to QoS prediction because it is accurate and scalable under many circumstances.Hence,state-of-the-art methods in QoS prediction are primarily based on LF.They improve the basic LF-based models by identifying the neighborhoods of QoS data based on some additional geographical information.However,the additional geographical information may be difficult to collect in considering information security,identity privacy,and commercial interests in real-world applications.Besides,they ignore the reliability of QoS data while unreliable ones are often mixed in.To address these issues,this paper proposes a dataaware latent factor(DALF)model to achieve highly accurate QoS prediction,where data-aware means DALF can easily implement the predictions according to the characteristics of QoS data.The main idea is to incorporate a density peaks based clustering method into an LF model to discover the neighborhoods and unreliable ones of QoS data.Experimental results on two benchmark real-world web service QoS datasets demonstrate that DALF has better performance than the state-of-the-art models.

作者: Di Wu Xin Luo Mingsheng Shang Yi He Guoyin Wang Xindong Wu

作者单位: Chongqing Key Laboratory of Big Data and Intelligent Computing,Chongqing Institute of Green and Inte University of Louisiana at Lafayette,Lafayette 70503,USA Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommuni

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 384-399

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）