Improving Tensor Based Recommenders with Clustering

Martin Leginus; Peter Dolog; Valdas Zemaitis

doi:10.1007/978-3-642-31454-4_13

Improving Tensor Based Recommenders with Clustering

Martin Leginus, Peter Dolog, Valdas Zemaitis

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

14 Citationer (Scopus)

Abstract

Social tagging systems (STS) model three types of entities
(i.e. tag-user-item) and relationships between them are encoded into a
3-order tensor. Latent relationships and patterns can be discovered by
applying tensor factorization techniques like Higher Order Singular Value
Decomposition (HOSVD), Canonical Decomposition etc. STS accumulate large amount of sparse data that restricts factorization techniques
to detect latent relations and also signiﬁcantly slows down the process
of a factorization. We propose to reduce tag space by exploiting clustering techniques so that the quality of the recommendations and execution time are improved and memory requirements are decreased. The
clustering is motivated by the fact that many tags in a tag space are
semantically similar thus the tags can be grouped. Finally, promising
experimental results are presented

Originalsprog	Engelsk
Titel	User Modeling, Adaptation, and Personalization : 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings
Antal sider	10
Vol/bind	7379
Udgivelsessted	Berlin
Forlag	Springer
Publikationsdato	2012
Sider	151-163
ISBN (Trykt)	978-3-642-31453-7
ISBN (Elektronisk)	978-3-642-31454-4
DOI	https://doi.org/10.1007/978-3-642-31454-4_13
Status	Udgivet - 2012
Begivenhed	User Modeling, Adaptation, and Personalization - Montreal, Canada Varighed: 16 jul. 2012 → 20 jul. 2012 Konferencens nummer: 20

Konference

Konference	User Modeling, Adaptation, and Personalization
Nummer	20
Land/Område	Canada
By	Montreal
Periode	16/07/2012 → 20/07/2012

Navn	Lecture Notes in Computer Science
Vol/bind	7379
ISSN	0302-9743

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10.1007/978-3-642-31454-4_13

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Link to publication in Scopus

MEco: MEco - Medical Ecosystem - Personalized Event-Based Surveillance
Dolog, P. (Projektleder), Xu, G. (Projektdeltager), Lage, R. G. (Projektdeltager), Bayyapu, K. R. (Projektdeltager) & Pan, R. (Projektdeltager)
FP7 IST
01/01/2010 → 30/06/2012
Projekter: Projekt › Forskning

Citationsformater

@inproceedings{36a8a0aafce44f8fb7e337f1b5c27e55,

title = "Improving Tensor Based Recommenders with Clustering",

abstract = "Social tagging systems (STS) model three types of entities(i.e. tag-user-item) and relationships between them are encoded into a3-order tensor. Latent relationships and patterns can be discovered byapplying tensor factorization techniques like Higher Order Singular ValueDecomposition (HOSVD), Canonical Decomposition etc. STS accumulate large amount of sparse data that restricts factorization techniquesto detect latent relations and also signiﬁcantly slows down the processof a factorization. We propose to reduce tag space by exploiting clustering techniques so that the quality of the recommendations and execution time are improved and memory requirements are decreased. Theclustering is motivated by the fact that many tags in a tag space aresemantically similar thus the tags can be grouped. Finally, promisingexperimental results are presented",

keywords = "tensor factorization, HOSVD, clustering.",

author = "Martin Leginus and Peter Dolog and Valdas Zemaitis",

year = "2012",

doi = "10.1007/978-3-642-31454-4_13",

language = "English",

isbn = "978-3-642-31453-7",

volume = "7379",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "151--163",

booktitle = "User Modeling, Adaptation, and Personalization",

address = "Germany",

note = "User Modeling, Adaptation, and Personalization, UMAP ; Conference date: 16-07-2012 Through 20-07-2012",

}

Leginus, M, Dolog, P & Zemaitis, V 2012, Improving Tensor Based Recommenders with Clustering. i User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings. bind 7379, Springer, Berlin, Lecture Notes in Computer Science, bind 7379, s. 151-163, User Modeling, Adaptation, and Personalization, Montreal, Canada, 16/07/2012. https://doi.org/10.1007/978-3-642-31454-4_13

Improving Tensor Based Recommenders with Clustering. / Leginus, Martin; Dolog, Peter; Zemaitis, Valdas.
User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings. Bind 7379 Berlin: Springer, 2012. s. 151-163 (Lecture Notes in Computer Science, Bind 7379).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Improving Tensor Based Recommenders with Clustering

AU - Leginus, Martin

AU - Dolog, Peter

AU - Zemaitis, Valdas

N1 - Conference code: 20

PY - 2012

Y1 - 2012

N2 - Social tagging systems (STS) model three types of entities(i.e. tag-user-item) and relationships between them are encoded into a3-order tensor. Latent relationships and patterns can be discovered byapplying tensor factorization techniques like Higher Order Singular ValueDecomposition (HOSVD), Canonical Decomposition etc. STS accumulate large amount of sparse data that restricts factorization techniquesto detect latent relations and also signiﬁcantly slows down the processof a factorization. We propose to reduce tag space by exploiting clustering techniques so that the quality of the recommendations and execution time are improved and memory requirements are decreased. Theclustering is motivated by the fact that many tags in a tag space aresemantically similar thus the tags can be grouped. Finally, promisingexperimental results are presented

AB - Social tagging systems (STS) model three types of entities(i.e. tag-user-item) and relationships between them are encoded into a3-order tensor. Latent relationships and patterns can be discovered byapplying tensor factorization techniques like Higher Order Singular ValueDecomposition (HOSVD), Canonical Decomposition etc. STS accumulate large amount of sparse data that restricts factorization techniquesto detect latent relations and also signiﬁcantly slows down the processof a factorization. We propose to reduce tag space by exploiting clustering techniques so that the quality of the recommendations and execution time are improved and memory requirements are decreased. Theclustering is motivated by the fact that many tags in a tag space aresemantically similar thus the tags can be grouped. Finally, promisingexperimental results are presented

KW - tensor factorization

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U2 - 10.1007/978-3-642-31454-4_13

DO - 10.1007/978-3-642-31454-4_13

M3 - Article in proceeding

SN - 978-3-642-31453-7

VL - 7379

T3 - Lecture Notes in Computer Science

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BT - User Modeling, Adaptation, and Personalization

PB - Springer

CY - Berlin

T2 - User Modeling, Adaptation, and Personalization

Y2 - 16 July 2012 through 20 July 2012

ER -

Improving Tensor Based Recommenders with Clustering

Abstract

Konference

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Projekter

MEco: MEco - Medical Ecosystem - Personalized Event-Based Surveillance

Citationsformater