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

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

14 Citations (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

Original language	English
Title of host publication	User Modeling, Adaptation, and Personalization : 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings
Number of pages	10
Volume	7379
Place of Publication	Berlin
Publisher	Springer
Publication date	2012
Pages	151-163
ISBN (Print)	978-3-642-31453-7
ISBN (Electronic)	978-3-642-31454-4
DOIs	https://doi.org/10.1007/978-3-642-31454-4_13
Publication status	Published - 2012
Event	User Modeling, Adaptation, and Personalization - Montreal, Canada Duration: 16 Jul 2012 → 20 Jul 2012 Conference number: 20

Conference

Conference	User Modeling, Adaptation, and Personalization
Number	20
Country/Territory	Canada
City	Montreal
Period	16/07/2012 → 20/07/2012

Series	Lecture Notes in Computer Science
Volume	7379
ISSN	0302-9743

Keywords

tensor factorization
HOSVD
clustering.

Access to Document

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

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Cite this

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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",

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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. in User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings. vol. 7379, Springer, Berlin, Lecture Notes in Computer Science, vol. 7379, pp. 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. Vol. 7379 Berlin: Springer, 2012. p. 151-163 (Lecture Notes in Computer Science, Vol. 7379).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › 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

KW - HOSVD

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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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CY - Berlin

T2 - User Modeling, Adaptation, and Personalization

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ER -

Improving Tensor Based Recommenders with Clustering

Abstract

Conference

Keywords

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

Cite this

Improving Tensor Based Recommenders with Clustering

Abstract

Conference

Keywords

Access to Document

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Other files and links

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Projects

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

Cite this