ALBERT

Description:    Web service recommendation has become a hot yet fundamental research topic in service computing. The most popular technique is the Collaborative Filtering (CF) based on a user-item matrix. However, it cannot well capture the relationship between Web services and providers. To address this issue, we first design a cube model to explicitly describe the relationship among providers, consumers and Web services. And then, we present a Standard Deviation based Hybrid Collaborative Filtering (SD-HCF) for Web Service Recommendation ( WSRec ) and an Inverse consumer Frequency based User Collaborative Filtering (IF-UCF) for Potential Consumers Recommendation ( PCRec ). Finally, the decision-making process of bidirectional recommendation is provided for both providers and consumers. Sets of experiments are conducted on real-world data provided by Planet-Lab. In the experiment phase, we show how the parameters of SD-HCF impact on the prediction quality as well as demonstrate that the SD-HCF is much better than extant methods on recommendation quality, including the CF based on user, the CF based on item and general HCF. Experimental comparison between IF-UCF and UCF indicates the effectiveness of adding inverse consumer frequency to UCF. Content Type Journal Article Category Regular Paper Pages 1-21 DOI 10.1007/s10115-012-0562-1 Authors Jie Cao, Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China Zhiang Wu, Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China Youquan Wang, College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China Yi Zhuang, College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, China Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377