Skip to main content

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 51))

Abstract

A tremendous increase has taken place in the amount of online content. As a result, by using traditional approaches, service-relevant data becomes too big to be effectively processed. In order to solve this problem, an approach called clustering based collaborative filtering (CF) is proposed in this paper. Its objective is to recommend services collaboratively in the same clusters. It is a very successful approach in such settings where interaction can be done between data analysis and querying. However the large systems which have large data and users, the collaboration are many times delayed due to unrealistic runtimes. The proposed approach works in two stages. First, the services which are available are divided into small clusters for processing and then collaborative filtering algorithm is used in second stage on one of the clusters. It is estimated to decrease the online execution time of collaborative filtering algorithm because the number of the services in a cluster is much less than the entire services available on the web.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wu, X., Wu, G., Zhu, X.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (January 2014)

    Google Scholar 

  2. Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. University Press of Cambridge

    Google Scholar 

  3. Bellogín, A., Díez, F., Cantador, I.: An empirical comparison of social, collaborative filtering (CF), hybrid recommender. ACM Trans. Intell. Syst. Tech. 4(1), 1–37 (January 2013)

    Google Scholar 

  4. Zeng, W., Zhang, Q., Shang, M.: Can dissimilar users contribute to accuracy and diversity of personalized recommendation? IJMPC 21(10), 1217–1227 (June 2010)

    Google Scholar 

  5. Havens, T.C., Hall, L.O., Leckie, C., Palaniswami, M.: Fuzzy c-means algorithm for very large data. IEEE Trans. Fuzzy Syst. 20(6), 1130–1146 (December 2012)

    Google Scholar 

  6. Liu, Z., Zheng, Y., Li, P.: Clustering to find exemplar terms for key-phrase extraction. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 257–266, May 2009

    Google Scholar 

  7. Rodriguez, A., Chaovalitwongse, W., Zhe, L.: Master defect record retrieval using network based feature ass. IEEE Trans. Syst. Man Cybern. App. Rev. 40(3), 319–329 (October 2010)

    Google Scholar 

  8. Adomavicis, G., Zhang, J.: Stability of recommendation algorithms. ACM Trans. Inf. Syst. 30(4), 23:1–23:31 (August 2012)

    Google Scholar 

  9. Liu, X., Mei, H., Huang, G.: Discovering homogeneous web services community in the user centric web environment. IEEE Trans. Serv. Comput. 2(2), 167–181

    Google Scholar 

  10. Li, H.H., Tian, X., Du, X.Y.: A review based reputation evaluation approach for web services. Int. J. Comput. Sci. Tech. 249(5), 893–900 (Sep 2009)

    Google Scholar 

  11. Zielinnski, K., Szydlo, T., Szymacha, R.: Adaptive soa solution stack. IEEE Trans. Serv. Comput. 5(2), 149–163 (April-June 2012)

    Google Scholar 

  12. Shafer, J., Rixner, S.T., Cox, A.: The hadoop distributed file system (HDFS): balancing portability and performance. IEEE Int. Symp. Perform. Anal. Syst. S\W. doi: 10.1109/ISPASS.2010.5452045. pp. 122–133, 28–30 March 2010

  13. Kirankumar, R., Vijayakumari, R., Gangadhara, R.K.: Comparative analysis of google file system and hadoop distributed file system. IJAT CSE. 3(1), 24–25 (Feb 2014)

    Google Scholar 

  14. HDFS Guide [Online]. http://hadoop.apache.org/common/doc/current/hdfs_user_guide

  15. Li, M.J., Cheung, Y., Ng, M.: Agglomerative fuzzy k means clustering algorithm with selection of number of clusters. IEEE Trans. Knowl. Data Eng. 20(11), 1519–1534 (November 2008)

    Google Scholar 

  16. Zhao, Y., Fayyad, U., Karypis, G.: Hierarchical clustering algorithms for document datasets. Data Min. Knowl. Discov. 10(2), 141–168 (November 2005)

    Google Scholar 

  17. Platzer, C., Dustdar, S., Rosenberg, F.: Web service clustering using multi-dimensional angle as proximity measures. ACM Trans. Internet Tech. 9(3), 11:1–11:26 (July 2009)

    Google Scholar 

  18. Taherian, T.F., Niknam, T., Pourjafarian, N.: An efficient algorithm based on modified imperialist competitive algorithm & K means for data clustering. Eng. App. Artif. Intell. 24(2), 306–317 (March 2011)

    Google Scholar 

  19. Thilagavathi, G., Aparna, N., Srivaishnavi, D.: A survey on efficient hierarchical algorithm used in clustering. Int. J. Eng. 2(9), 306–317 (Sep 2013)

    Google Scholar 

  20. Julie, D., Kumar, K.: Optimal web service selection scheme with dynamic QoS property assignment. IJART. 2(2), 69–75 (May 2012)

    Google Scholar 

  21. Wu, J., Chen, L., Feng, Y.: Predicting quality of service for selection by neighborhood based collaborative filtering (CF). IEEE Trans. Syst. Man Cybern. Syst. 43(2), 428–439 (March 2013)

    Google Scholar 

  22. Lens G MovieLens. Available [Online] http://grouplens.org/datasets/movielens.html

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ganjendra R. Bamnote .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Agrawal, S.S., Bamnote, G.R. (2016). Implementing and Evaluating Collaborative Filtering (CF) Using Clustering. In: Satapathy, S., Das, S. (eds) Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2. Smart Innovation, Systems and Technologies, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-319-30927-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30927-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30926-2

  • Online ISBN: 978-3-319-30927-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics