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Knowledge and Information Systems

, Volume 57, Issue 3, pp 709–720 | Cite as

Two collaborative filtering recommender systems based on sparse dictionary coding

  • Ismail Emre Kartoglu
  • Michael W. Spratling
Regular Paper
  • 662 Downloads

Abstract

This paper proposes two types of recommender systems based on sparse dictionary coding. Firstly, a novel predictive recommender system that attempts to predict a user’s future rating of a specific item. Secondly, a top-n recommender system which finds a list of items predicted to be most relevant for a given user. The proposed methods are assessed using a variety of different metrics and are shown to be competitive with existing collaborative filtering recommender systems. Specifically, the sparse dictionary-based predictive recommender has advantages over existing methods in terms of a lower computational cost and not requiring parameter tuning. The sparse dictionary-based top-n recommender system has advantages over existing methods in terms of the accuracy of the predictions it makes and not requiring parameter tuning. An open-source software implemented and used for the evaluation in this paper is also provided for reproducibility.

Keywords

Recommender systems Algorithms Sparse coding Evaluation 

References

  1. 1.
    Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132CrossRefGoogle Scholar
  2. 2.
    Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, UAI’98, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc, pp 43–52Google Scholar
  3. 3.
    Bruckstein A, Donoho D, Elad M (2009) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cooper C, Lee S, Radzik T, Siantos Y (2014) Random walks in recommender systems: exact computation and simulations. In: Proceedings of the companion publication of the 23rd international conference on world wide web companion, WWW Companion ’14, Geneva, Switzerland. International World Wide Web Conferences Steering Committee, pp 811–816Google Scholar
  5. 5.
    Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst 22(1):143–177CrossRefGoogle Scholar
  6. 6.
    Elad M (2010) Sparse and redundant representations: from theory to applications in signal and image processing. Springer, BerlinCrossRefzbMATHGoogle Scholar
  7. 7.
    Fouss F, Pirotte A, Saerens M (2005) A novel way of computing similarities between nodes of a graph, with application to collaborative recommendation. In: Web intelligence, 2005. Proceedings. The 2005 IEEE/WIC/ACM international conference on, pp 550–556Google Scholar
  8. 8.
    Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: a constant time collaborative filtering algorithm. Inf Retr 4(2):133–151CrossRefzbMATHGoogle Scholar
  9. 9.
    Herlocker J, Konstan J, Terveen L, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22:5–53CrossRefGoogle Scholar
  10. 10.
    Hoyer P (2004) Non-negative matrix factorization with sparseness constraints. J Mach Learn Res 5:1457–1469MathSciNetzbMATHGoogle Scholar
  11. 11.
    Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37CrossRefGoogle Scholar
  12. 12.
    Ning X, Karypis G (2011) Slim: sparse linear methods for top-n recommender systems. In: Data mining (ICDM), 2011 IEEE 11th international conference, pp 497–506Google Scholar
  13. 13.
    Plumbley M (2006). Recovery of sparse representations by polytope faces pursuit. In: Independent Component Analysis and Blind Signal Separation. Springer, Berlin, pp 206–213Google Scholar
  14. 14.
    Sarwar B, Karypis G, Konstan J, Riedl J (2000) Application of dimensionality reduction in recommender system: a case study. Technical report, DTIC DocumentGoogle Scholar
  15. 15.
    Sarwar B, Karypis G, Konstan J, Riedl J (2001a) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, WWW ’01, New York. ACM, pp 285–295Google Scholar
  16. 16.
    Sarwar B, Karypis G, Konstan J, Riedl J (2001b) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web. ACM, pp 285–295Google Scholar
  17. 17.
    Spratling M (2014) Classification using sparse representations: a biologically plausible approach. Biol Cybern 108(1):61–73MathSciNetCrossRefGoogle Scholar
  18. 18.
    Szabó Z, Póczos B, Lőrincz A (2012) Collaborative filtering via group-structured dictionary learning. In: Latent variable analysis and signal separation, vol 7191. Lecture notes in computer science. Springer, Berlin, pp 247–254Google Scholar
  19. 19.
    Wright J, Ma Y, Mairal J, Sapiro G, Huang T, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044CrossRefGoogle Scholar
  20. 20.
    Zhou T, Kuscsik Z, Liu J, Medo M, Wakeling J, Zhang Y (2010) Solving the apparent diversity-accuracy dilemma of recommender systems. Proc Natl Acad Sci 107(10):4511–4515CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of InformaticsKing’s College LondonLondonUK

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