Two collaborative filtering recommender systems based on sparse dictionary coding

Ismail Emre Kartoglu*, Michael W. Spratling

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
274 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)709-720
Number of pages12
JournalKNOWLEDGE AND INFORMATION SYSTEMS
Volume57
Issue number3
Early online date15 Jan 2018
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Algorithms
  • Evaluation
  • Recommender systems
  • Sparse coding

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