Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation

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.

[1]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[2]  Korris Fu-Lai Chung,et al.  A probabilistic rating inference framework for mining user preferences from reviews , 2011, World Wide Web.

[3]  Li Chunlin,et al.  Optimization decomposition approach for layered QoS scheduling in grid computing , 2007 .

[4]  Zili Zhang,et al.  Missing Value Estimation for Mixed-Attribute Data Sets , 2011, IEEE Transactions on Knowledge and Data Engineering.

[5]  Athman Bouguettaya,et al.  Rater Credibility Assessment in Web Services Interactions , 2009, World Wide Web.

[6]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[7]  David E. Culler,et al.  PlanetLab: an overlay testbed for broad-coverage services , 2003, CCRV.

[8]  Kamal Ali,et al.  TiVo: making show recommendations using a distributed collaborative filtering architecture , 2004, KDD.

[9]  Yehuda Koren,et al.  Improved Neighborhood-based Collaborative Filtering , 2007 .

[10]  Zibin Zheng,et al.  Collaborative reliability prediction of service-oriented systems , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.

[11]  Xi Chen,et al.  RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation , 2010, 2010 IEEE International Conference on Web Services.

[12]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[13]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[14]  Douglas A. Wolfe,et al.  Nonparametric Statistical Methods , 1973 .

[15]  Byron Leite Dantas Bezerra,et al.  Symbolic data analysis tools for recommendation systems , 2011 .

[16]  Yehuda Koren,et al.  Lessons from the Netflix prize challenge , 2007, SKDD.

[17]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[18]  Chia-Hui Chang,et al.  Sentiment-oriented contextual advertising , 2009, Knowledge and Information Systems.

[19]  Christopher Meek,et al.  A unified approach to building hybrid recommender systems , 2009, RecSys '09.

[20]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[21]  Zibin Zheng,et al.  WSRec: A Collaborative Filtering Based Web Service Recommender System , 2009, 2009 IEEE International Conference on Web Services.

[22]  Jiuxin Cao,et al.  QoS Guaranteed Service Resource Co-Allocation and Management: QoS Guaranteed Service Resource Co-Allocation and Management , 2009 .

[23]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[24]  Jonathan L. Herlocker,et al.  A collaborative filtering algorithm and evaluation metric that accurately model the user experience , 2004, SIGIR '04.

[25]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[26]  Yu Li,et al.  A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce , 2005, Expert Syst. Appl..

[27]  GeunSik Jo,et al.  Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation , 2010, Electron. Commer. Res. Appl..

[28]  Kristen LeFevre,et al.  A privacy recommendation wizard for users of social networking sites , 2010, CCS '10.

[29]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[30]  P. Sprent,et al.  Applied nonparametric statistical methods , 1988 .

[31]  Wu Zhi.  QoS Guaranteed Service Resource Co-Allocation and Management , 2009 .

[32]  Robert A. Legenstein,et al.  Combining predictions for accurate recommender systems , 2010, KDD.

[33]  Xiaofeng Zhu,et al.  Missing data imputation by utilizing information within incomplete instances , 2011, J. Syst. Softw..

[34]  Luca Becchetti,et al.  Recommending items in pervasive scenarios: models and experimental analysis , 2011, Knowledge and Information Systems.

[35]  Yang Song,et al.  Automatic tag recommendation algorithms for social recommender systems , 2011, ACM Trans. Web.

[36]  Chunlin Li,et al.  Optimization decomposition approach for layered QoS scheduling in grid computing , 2007, J. Syst. Archit..

[37]  P. Sprent,et al.  19. Applied Nonparametric Statistical Methods , 1995 .

[38]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[39]  Zhaohui Wu,et al.  Computing compatibility in dynamic service composition , 2009, Knowledge and Information Systems.