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 KartogluEmail author
  • Michael W. Spratling
Regular Paper


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.


Recommender systems Algorithms Sparse coding Evaluation 


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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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