Knowledge and Information Systems

, Volume 36, Issue 3, pp 607–627 | Cite as

Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation

  • Jie Cao
  • Zhiang WuEmail author
  • Youquan Wang
  • Yi Zhuang
Regular Paper


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.


Web service Bidirectional recommendation Collaborative Filtering  Hybrid Collaborative Filtering 



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).


  1. 1.
    Ali K, Stam W (2004) TiVo: making show recommendations using a distributed collaborative filtering architecture. In: Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’04), pp 394–401Google Scholar
  2. 2.
    Becchetti L, Colesanti UM, Marchetti-Spaccamela A, Vitaletti A (2011) Recommending items in pervasive scenarios: models and experimental analysis. Knowl Inf Syst (KAIS) 28(3):555–578CrossRefGoogle Scholar
  3. 3.
    Bell RM, Koren Y (2007) Improved neighborhood-based collaborative filtering. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’07). California, pp 7–14Google Scholar
  4. 4.
    Bell RM, Koren Y (2007) Lessons from the Netflix prize challenge. SIGKDD Explor 9(2):75–79CrossRefGoogle Scholar
  5. 5.
    Bezerra B, Carvalho F (2011) Symbolic data analysis tools for recommendation systems. Knowl Inf Syst (KAIS) 26(3):385–418CrossRefGoogle Scholar
  6. 6.
    Chen X, Liu X, Huang Z, Sun H (2010) RegionKNN: a scalable hybrid collaborative filtering algorithm for personalized web service recommendation. In: 2010 IEEE international conference on web services, pp 9–16Google Scholar
  7. 7.
    Chun B, Culler D, Roscoe T, Bavier A, Peterson L, Wawrzoniak M, Bowman M (2003) Planetlab: an overlay testbed for broad-coverage services. ACM SIGCOMM comput Commun Rev 33(3):3–12CrossRefGoogle Scholar
  8. 8.
    Fan T, Chang C (2010) Sentiment-oriented contextual advertising. Knowl Inf Syst (KAIS) 23(2):321–344CrossRefGoogle Scholar
  9. 9.
    Fang L, Kim H, LeFevre K, Tami A (2010) A privacy recommendation wizard for users of social networking sites. In: Proceedings of the 17th ACM conference on computer and communications, Security, pp 630–632Google Scholar
  10. 10.
    Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70CrossRefGoogle Scholar
  11. 11.
    Gunawardana A, Meek C (2009) A unified approach to building hybrid recommender systems. In: Proceedings of ACM conference on recommender systems (RecSys’09). New York, USA, pp 117–124Google Scholar
  12. 12.
    Herlocker J, Konstan J, Terveen L, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53CrossRefGoogle Scholar
  13. 13.
    Jahrer M, Töscher A, Legenstein R (2010) Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’10), pp 693–701Google Scholar
  14. 14.
    Kim H, Ji A, Ha I, Jo G (2010) Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electron Commer Res Appl 9(1):73–83CrossRefGoogle Scholar
  15. 15.
    Konstan J, Miller B, Maltz D, Herlocker J, Gordon L, Riedl J (1997) GroupLens: applying collaborative filtering to Usenet news. Commun ACM 40(3):77–87CrossRefGoogle Scholar
  16. 16.
    Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’08), pp 426–434Google Scholar
  17. 17.
    Koren Y (2009) Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data 4(1):1–24CrossRefGoogle Scholar
  18. 18.
    Leung C, Chan S, Chung F, Ngai G (2011) A probabilistic rating inference framework for mining user preferences from reviews. World Wide Web Internet Web Inf Syst 14(2):187–215Google Scholar
  19. 19.
    Li C, Li L (2007) Optimization decomposition approach for layered QoS scheduling in grid computing. J Syst Archit 53(11):816–823CrossRefGoogle Scholar
  20. 20.
    Li Y, Liu L, Li X (2005) A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce. Expert Syst Appl 28(1):67–77CrossRefGoogle Scholar
  21. 21.
    Linden G, Smith B, York J (2003) recommendations: item-to-item collaborative filtering. IEEE Intern Comput 7(1):76–80CrossRefGoogle Scholar
  22. 22.
    Malik Z, Rater BA (2009) Credibility assessment in web services interactions. World Wide Web Internet Web Inf Syst 12(1):3–25Google Scholar
  23. 23.
    Mclaughlin MR, Herlocker JL (2004) A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in, information retrieval(SIGIR’04), pp 329–336Google Scholar
  24. 24.
    Melville P, Mooney R, Nagarajan R (2002) Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the 18th national conference on, artificial intelligence (AAAI’02), pp 187–192Google Scholar
  25. 25.
    Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-Based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international world wide web conference, pp 285–295Google Scholar
  26. 26.
    Sprent P, Smeeton NC (2000) Applied nonparametric statistical methods, 3rd edn. Chapman& Hall, LondonCrossRefGoogle Scholar
  27. 27.
    Tao Y, Zhang Y, Lin K (2007) Effective algorithms for web services selection with end-to-end QoS constraints. ACM Trans Web (TWEB) 1(1):1–26CrossRefGoogle Scholar
  28. 28.
    Wu Z, Deng S, Li Y, Wu J (2009) Computing compatibility in dynamic service composition. Knowl Inf Syst (KAIS) 19(1):107–129MathSciNetCrossRefGoogle Scholar
  29. 29.
    Wu Z, Luo J, Song A, Cao J (2009) QoS guaranteed service resource co-allocation and management. J Softw 12(20):3150–3162CrossRefGoogle Scholar
  30. 30.
    Yang S, Zhang L, Giles C (2011) Automatic tag recommendation algorithms for social. ACM Trans Web (TWEB) 5(1): 31 Article 4Google Scholar
  31. 31.
    Zeng L, Benatallah B, Ngu AH, Dumas M, Kalagnanam J, Chang H (2004) QoS-aware middleware for web services composition. IEEE Trans Softw Eng 30(1):311–327CrossRefGoogle Scholar
  32. 32.
    Zhang S, Jin Z, Zhu X (2011) Missing data imputation by utilizing information within incomplete instances. J Syst Softw 84(3):452–459CrossRefGoogle Scholar
  33. 33.
    Zheng Z, Ma H, Michael R, King I (2009) WSRec: a collaborative filtering based web service recommender system. In: IEEE International Conference on Web Services, pp 437–449Google Scholar
  34. 34.
    Zheng Z, Michael R (2010) Collaborative reliability prediction for service-oriented systems. In: Proceedings of the ACM/IEEE 32nd international conference on software engineering (ICSE’10). Cape Town, South Africa, pp 35–44Google Scholar
  35. 35.
    Zhu X, Zhang S, Jin Z, Zhang Z, Xu Z (2011) Missing value estimation for mixed-attribute data sets. IEEE Trans Knowl Data Eng 23(1):110–121CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.Jiangsu Provincial Key Laboratory of E-BusinessNanjing University of Finance and EconomicsNanjingChina
  2. 2.College of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  3. 3.College of Computer and Information EngineeringZhejiang Gongshang UniversityHangzhouChina

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