Improving Tensor Based Recommenders with Clustering

Martin Leginus, Peter Dolog, Valdas Zemaitis

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

10 Citationer (Scopus)

Resumé

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
OriginalsprogEngelsk
TitelUser Modeling, Adaptation, and Personalization : 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings
Antal sider10
Vol/bind7379
Udgivelses stedBerlin
ForlagSpringer
Publikationsdato2012
Sider151-163
ISBN (Trykt)978-3-642-31453-7
ISBN (Elektronisk)978-3-642-31454-4
DOI
StatusUdgivet - 2012
BegivenhedUser Modeling, Adaptation, and Personalization - Montreal, Canada
Varighed: 16 jul. 201220 jul. 2012
Konferencens nummer: 20

Konference

KonferenceUser Modeling, Adaptation, and Personalization
Nummer20
LandCanada
ByMontreal
Periode16/07/201220/07/2012
NavnLecture Notes in Computer Science
Vol/bind7379
ISSN0302-9743

Fingerprint

Factorization
Tensors
Decomposition
Data storage equipment

Citer dette

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, s. 151-163). Berlin: Springer. Lecture Notes in Computer Science, Bind. 7379 https://doi.org/10.1007/978-3-642-31454-4_13
Leginus, Martin ; Dolog, Peter ; Zemaitis, Valdas. / Improving Tensor Based Recommenders with Clustering. 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).
@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",
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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, 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 proceedingKonferenceartikel i proceedingForskningpeer review

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Leginus M, Dolog P, Zemaitis V. 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. Berlin: Springer. 2012. s. 151-163. (Lecture Notes in Computer Science, Bind 7379). https://doi.org/10.1007/978-3-642-31454-4_13