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Knowledge and Information Systems

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15-01-2018 | Regular Paper

Two collaborative filtering recommender systems based on sparse dictionary coding

Authors: Ismail Emre Kartoglu, Michael W. Spratling

Published in: Knowledge and Information Systems | Issue 3/2018

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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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Title: Two collaborative filtering recommender systems based on sparse dictionary coding
Authors: Ismail Emre Kartoglu
Michael W. Spratling
Publication date: 15-01-2018
Publisher: Springer London
Published in: Knowledge and Information Systems / Issue 3/2018
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-018-1157-2

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