Skip to main content
SpringerLink
Log in

A novel similarity measure towards effective recommendation using Matusita coefficient for Collaborative Filtering in a sparse dataset

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Collaborative Filtering (CF) is a prominent approach to ensure personalized recommendations to active online users. An efficient CF is the memory-based strategy that finds nearest neighbours to an active user using conventional similarity measures. Most such measures deal with a co-rated item rated by a pair of users and hence they are not appropriate to provide an effective recommendation to a sparse dataset having less co-rated items. This study proposes a novel similarity measure, Matusita coefficient in CF (MCF), which considers all ratings given by a user to estimate nearest neighbours. MCF considers local and global rating information provided by users on different rating scales. The performance of the proposed measure is examined and checked by comparing it to conventional measures using popular benchmark datasets like MovieLens and Netflix. The recommendation results demonstrate that the proposed measure outperforms conventional similarity measures on various performance metrics like Mean Absolute Error, Root Mean Squared Error, accuracy, precision, recall and coverage.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Basu Chumki, Hirsh Haym, Cohen William, et al 1998 Recommendation as classification: Using social and content-based information in recommendation. In: Aaai/iaai, pages 714–720

  2. Senecal Sylvain and Nantel Jacques 2004 The influence of online product recommendations on consumers online choices. Journal of Retailing, 80(2): 159–169

    Article  Google Scholar 

  3. Mooney Raymond J and Roy Loriene 2000 Content-based book recommending using learning for text categorization. In: Proceedings of the fifth ACM conference on Digital libraries, pages 195–204. ACM

  4. Hicken Wendell, Holm Frode, Clune James and Campbell Marc 2004 Music recommendation system and method, August 13. US Patent App. 10/917,865

  5. Davidson James, Liebald Benjamin, Liu Junning, Nandy Palash, Van Vleet Taylor, Gargi Ullas, Gupta Sujoy, He Yu, Lambert Mike, Livingston Blake, et al 2010 The youtube video recommendation system. In: Proceedings of the fourth ACM conference on Recommender systems, pages 293–296. ACM

  6. Sinha Rashmi R and Swearingen Kirsten 2001 Comparing recommendations made by online systems and friends. In: DELOS workshop: personalisation and recommender systems in digital libraries, volume 106

  7. Kabassi Katerina 2010 Personalizing recommendations for tourists. Telematics and Informatics 27(1): 51–66

    Article  Google Scholar 

  8. Burke Robin 2002 Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4): 331–370

    Article  Google Scholar 

  9. Lang Ken 1995 Newsweeder: Learning to filter netnews. In: Proceedings of the 12th international conference on machine learning, pages 331–339

  10. Lops Pasquale, De Gemmis Marco and Semeraro Giovanni 2011 Content-based recommender systems: State of the art and trends. In: Recommender systems handbook, pages 73–105. Springer

  11. Adomavicius Gediminas and Tuzhilin Alexander 2005 Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17(6): 734–749

  12. Bobadilla Jesús, Ortega Fernando, Hernando Antonio and Gutiérrez Abraham 2013 Recommender systems survey. Knowledge-based Systems 46: 109–132

    Article  Google Scholar 

  13. Breese John S, Heckerman David and Kadie Carl 1998 Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pages 43–52. Morgan Kaufmann Publishers Inc

  14. Cacheda Fidel, Carneiro Víctor, Fernández Diego and Formoso Vreixo 2011 Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web (TWEB), 5(1): 2

    Google Scholar 

  15. Koren Yehuda 2010 Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data (TKDD), 4(1): 1

    Google Scholar 

  16. Ning Xia, Desrosiers Christian and Karypis George 2015 A comprehensive survey of neighborhood-based recommendation methods. In: Recommender systems handbook, pages 37–76. Springer

  17. Yildirim Hilmi and Krishnamoorthy Mukkai S 2008 A random walk method for alleviating the sparsity problem in collaborative filtering. In: Proceedings of the 2008 ACM conference on Recommender systems, pages 131–138. ACM

  18. Shardanand Upendra and Maes Pattie 1995 Social information filtering: algorithms for automating word of mouth. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 210–217. ACM Press/Addison-Wesley Publishing Co

  19. Herlocker Jonathan L, Konstan Joseph A, Borchers Al and Riedl John 1999 An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 230–237. ACM

  20. Jamali Mohsen and Ester Martin 2009 Trustwalker: a random walk model for combining trust-based and item-based recommendation. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 397–406. ACM

  21. Salton Gerard and McGill Michael J 1986 Introduction to modern information retrieval

  22. Sarwar Badrul, Karypis George, Konstan Joseph and Riedl John 2001 Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pages 285–295. ACM

  23. Herlocker Jonathan L, Konstan Joseph A, Terveen Loren G and Riedl John T 2004 Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1): 5–53

    Article  Google Scholar 

  24. Koutrika Georgia, Bercovitz Benjamin and Garcia-Molina Hector 2009 Flexrecs: expressing and combining flexible recommendations. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pages 745–758. ACM

  25. Bobadilla Jesús, Serradilla Francisco and Bernal Jesus 2010 A new collaborative filtering metric that improves the behavior of recommender systems. Knowledge-Based Systems 23(6): 520–528

    Article  Google Scholar 

  26. Liu Haifeng, Hu Zheng, Mian Ahmad, Tian Hui and Zhu Xuzhen 2014 A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems 56: 156–166

    Article  Google Scholar 

  27. Patra Bidyut Kr, Launonen Raimo, Ollikainen Ville and Nandi Sukumar 2014 Exploiting bhattacharyya similarity measure to diminish user cold-start problem in sparse data. In: International Conference on Discovery Science, pages 252–263. Springer

  28. Patra Bidyut Kr, Launonen Raimo, Ollikainen Ville and Nandi Sukumar 2015 A new similarity measure using bhattacharyya coefficient for collaborative filtering in sparse data. Knowledge-Based Systems 82: 163–177

    Article  Google Scholar 

  29. Fu King-Sun et al 1976 Pattern recognition and image processing. IEEE Transactions on Computers 100(12): 1336–1346

    Article  Google Scholar 

  30. Basseville Michele 1989 Distance measures for signal processing and pattern recognition. Signal processing 18(4): 349–369

    Article  MathSciNet  Google Scholar 

  31. Nikulin M S 2001 Hellinger distance. hazewinkel, michiel, encyclopedia of mathematics. Springer, Berlin. doi, 10: 1361684–1361686

  32. Bobadilla Jesús, Ortega Fernando and Hernando Antonio 2012 A collaborative filtering similarity measure based on singularities. Information Processing & Management 48(2): 204–217

    Article  Google Scholar 

  33. Aherne Frank J, Thacker Neil A and Rockett Peter I 1998 The bhattacharyya metric as an absolute similarity measure for frequency coded data. Kybernetika 34(4): 363–368

    MathSciNet  MATH  Google Scholar 

  34. Bobadilla Jesus, Hernando Antonio, Ortega Fernando and Bernal Jesus 2011 A framework for collaborative filtering recommender systems. Expert Systems with Applications 38(12): 14609–14623

    Article  Google Scholar 

  35. Movielens dataset. http://www.grouplens.org

  36. Netflix dataset. http://www.netflixprize.com

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, 620015, India

    C Selvi & E Sivasankar

Authors
  1. C Selvi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E Sivasankar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C Selvi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Selvi, C., Sivasankar, E. A novel similarity measure towards effective recommendation using Matusita coefficient for Collaborative Filtering in a sparse dataset. Sādhanā 43, 202 (2018). https://doi.org/10.1007/s12046-018-0970-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-018-0970-3

Keywords

Search

Navigation