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Two collaborative filtering recommender systems based on sparse dictionary coding

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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.

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Notes

  1. http://grouplens.org/datasets/movielens/.

  2. http://www.ieor.berkeley.edu/~goldberg/jester-data/.

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Correspondence to Ismail Emre Kartoglu.

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Kartoglu, I.E., Spratling, M.W. Two collaborative filtering recommender systems based on sparse dictionary coding. Knowl Inf Syst 57, 709–720 (2018). https://doi.org/10.1007/s10115-018-1157-2

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  • DOI: https://doi.org/10.1007/s10115-018-1157-2

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