Collaborative filtering via group-structured dictionary learning

Zoltán Szabó, Barnabás Póczos, András Lorincz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the presented method outperforms its state-of-the-art competitors and has several advantages over approaches that do not put structured constraints on the dictionary elements.

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
Pages247-254
Number of pages8
DOIs
Publication statusPublished - Feb 27 2012
Event10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 - Tel Aviv, Israel
Duration: Mar 12 2012Mar 15 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7191 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
CountryIsrael
CityTel Aviv
Period3/12/123/15/12

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Keywords

  • collaborative filtering
  • structured dictionary learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Szabó, Z., Póczos, B., & Lorincz, A. (2012). Collaborative filtering via group-structured dictionary learning. In Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings (pp. 247-254). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7191 LNCS). https://doi.org/10.1007/978-3-642-28551-6_31