EigenRec: generalizing PureSVD for effective and efficient top-N recommendations

Athanasios Nikolakopoulos, Vassilis Kalantzis, Efstratios Gallopoulos, John D. Garofalakis

Research output: Contribution to journalArticlepeer-review

10 Scopus citations
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Abstract

We introduce EigenRec, a versatile and efficient latent factor framework for top-N recommendations that includes the well-known PureSVD algorithm as a special case. EigenRec builds a low-dimensional model of an inter-item proximity matrix that combines a similarity component, with a scaling operator, designed to control the influence of the prior item popularity on the final model. Seeing PureSVD within our framework provides intuition about its inner workings, exposes its inherent limitations, and also, paves the path toward painlessly improving its recommendation performance. A comprehensive set of experiments on the MovieLens and the Yahoo datasets based on widely applied performance metrics, indicate that EigenRec outperforms several state-of-the-art algorithms, in terms of Standard and Long-Tail recommendation accuracy, exhibiting low susceptibility to sparsity, even in its most extreme manifestations—the Cold-Start problems. At the same time, EigenRec has an attractive computational profile and it can apply readily in large-scale recommendation settings.

Original languageEnglish (US)
Pages (from-to)59-81
Number of pages23
JournalKnowledge and Information Systems
Volume58
Issue number1
DOIs
StatePublished - Jan 8 2019

Bibliographical note

Funding Information:
Acknowledgements Vassilis Kalantzis was partially supported by a Gerondelis Foundation Fellowship. The authors acknowledge the Minnesota Supercomputing Institute (http://www.msi.umn.edu) at the University of Minnesota for providing resources that contributed to the research results reported within this paper.

Funding Information:
Vassilis Kalantzis was partially supported by a Gerondelis Foundation Fellowship. The authors acknowledge the Minnesota Supercomputing Institute (http://www.msi.umn.edu) at the University of Minnesota for providing resources that contributed to the research results reported within this paper.

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

Keywords

  • Collaborative filtering
  • Distributed computing
  • Latent factor methods
  • PureSVD
  • Sparsity
  • Top-N recommendation

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