Presentation	2014-08-20 Tensor Factorization that utilizes Linked Open Data Makoto NAKATSUJI, Hiroyuki TODA, Hiroshi SAWADA,
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Abstract(in English)	Human activities are usually represented as multi-object relationships (e.g. user's tagging activities for items or user's tweeting activities at some locations). Since multi-object relationships are naturally represented as a tensor, tensor factorization is becoming more important for predicting users' possible activities. However, its prediction accuracy is weak for ambiguous and/or sparsely observed objects. Our solution, Semantic data Representation for Tensor Factorization (SRTF), tackles these problems by incorporating semantics into tensor factorization. Experiments show that SRTF achieves higher accuracy than state-of-the-art methods.
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Keyword(in English)	Linked Open Data / Tensor Factorization / Semantic Web
Paper #	AI2014-14,SC2014-11
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Conference Information
Committee	SC
Conference Date	2014/8/13(1days)
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Paper Information
Registration To	Services Computing (SC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Tensor Factorization that utilizes Linked Open Data
Sub Title (in English)
Keyword(1)	Linked Open Data
Keyword(2)	Tensor Factorization
Keyword(3)	Semantic Web
1st Author's Name	Makoto NAKATSUJI
1st Author's Affiliation	NTT Service Evolution Laboratories()
2nd Author's Name	Hiroyuki TODA
2nd Author's Affiliation	NTT Service Evolution Laboratories
3rd Author's Name	Hiroshi SAWADA
3rd Author's Affiliation	NTT Service Evolution Laboratories
Date	2014-08-20
Paper #	AI2014-14,SC2014-11
Volume (vol)	vol.114
Number (no)	182
Page	pp.pp.-
#Pages	6
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