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Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation

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Abstract

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.

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Notes

  1. https://www.wsdream.net.

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Acknowledgments

This research is supported by National Natural Science Foundation of China (Nos. 71072172, 61103229, 61003074), Industry Projects in the Jiangsu S&T Pillar Program (No. BE2011198), Jiangsu Provicial Colleges and Universities Outstanding S&T Innovation Team Fund (No. 2001013), Key Project of Natural Science Research in Jiangsu Provincial Colleges and Universities (No. 12KJA520001), National Key Technologies R&D sub Program in 12th five-year-plan (No. SQ2011GX07E03990), International S&T Cooperation Program of China (No. 2011DFA12910), Program of Natural Science Foundation of Zhejiang Province (Nos. Z1100822, Y1110644, Y1110969, Y1090165) and Key Laboratory of Network and Information Security of Jiangsu Province of China (Southeast University) (No. BM2003201).

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Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China

    Jie Cao & Zhiang Wu

  2. College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Youquan Wang

  3. College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, China

    Yi Zhuang

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  1. Jie Cao
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  2. Zhiang Wu
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Correspondence to Zhiang Wu.

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Cao, J., Wu, Z., Wang, Y. et al. Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation. Knowl Inf Syst 36, 607–627 (2013). https://doi.org/10.1007/s10115-012-0562-1

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