Collaborative filtering via group-structured dictionary learning

Zoltán Szabó, Barnabás Póczos, A. Lőrincz

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages247-254
Number of pages8
Volume7191 LNCS
DOIs
Publication statusPublished - 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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Collaborative filtering
Collaborative Filtering
Glossaries
Sparse Coding
Recommender Systems
Recommender systems
Learning systems
Machine Learning
Numerical Experiment
Demonstrate
Dictionary
Learning
Experiments

Keywords

  • collaborative filtering
  • structured dictionary learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Szabó, Z., Póczos, B., & Lőrincz, A. (2012). Collaborative filtering via group-structured dictionary learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7191 LNCS, 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

Collaborative filtering via group-structured dictionary learning. / Szabó, Zoltán; Póczos, Barnabás; Lőrincz, A.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7191 LNCS 2012. p. 247-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7191 LNCS).

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

Szabó, Z, Póczos, B & Lőrincz, A 2012, Collaborative filtering via group-structured dictionary learning. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7191 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7191 LNCS, pp. 247-254, 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012, Tel Aviv, Israel, 3/12/12. https://doi.org/10.1007/978-3-642-28551-6_31
Szabó Z, Póczos B, Lőrincz A. Collaborative filtering via group-structured dictionary learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7191 LNCS. 2012. p. 247-254. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-28551-6_31
Szabó, Zoltán ; Póczos, Barnabás ; Lőrincz, A. / Collaborative filtering via group-structured dictionary learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7191 LNCS 2012. pp. 247-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{cd23625a20194d2bbe245d380d0ed3e8,
title = "Collaborative filtering via group-structured dictionary learning",
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.",
keywords = "collaborative filtering, structured dictionary learning",
author = "Zolt{\'a}n Szab{\'o} and Barnab{\'a}s P{\'o}czos and A. Lőrincz",
year = "2012",
doi = "10.1007/978-3-642-28551-6_31",
language = "English",
isbn = "9783642285509",
volume = "7191 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "247--254",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Collaborative filtering via group-structured dictionary learning

AU - Szabó, Zoltán

AU - Póczos, Barnabás

AU - Lőrincz, A.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - collaborative filtering

KW - structured dictionary learning

UR - http://www.scopus.com/inward/record.url?scp=84857302205&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84857302205&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-28551-6_31

DO - 10.1007/978-3-642-28551-6_31

M3 - Conference contribution

SN - 9783642285509

VL - 7191 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 247

EP - 254

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -