A probabilistic framework of preference discovery from folksonomy corpus

Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framew...
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Autor*in:	Guo, Xiaohui [verfasserIn] Hu, Chunming [verfasserIn] Zhang, Richong [verfasserIn] Huai, Jinpeng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	preference discovery tagging folksonomy social annotation

Übergeordnetes Werk:	Enthalten in: Frontiers of computer science in China - Beijing : Higher Education Press, 2007, 11(2016), 6 vom: 29. Juni, Seite 1075-1084
Übergeordnetes Werk:	volume:11 ; year:2016 ; number:6 ; day:29 ; month:06 ; pages:1075-1084

Links:	Volltext

DOI / URN:	10.1007/s11704-016-5132-3

Katalog-ID:	SPR021940592

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allfields	10.1007/s11704-016-5132-3 doi (DE-627)SPR021940592 (SPR)s11704-016-5132-3-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Guo, Xiaohui verfasserin aut A probabilistic framework of preference discovery from folksonomy corpus 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered. preference discovery (dpeaa)DE-He213 tagging (dpeaa)DE-He213 folksonomy (dpeaa)DE-He213 social annotation (dpeaa)DE-He213 Hu, Chunming verfasserin aut Zhang, Richong verfasserin aut Huai, Jinpeng verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 11(2016), 6 vom: 29. Juni, Seite 1075-1084 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:11 year:2016 number:6 day:29 month:06 pages:1075-1084 https://dx.doi.org/10.1007/s11704-016-5132-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 11 2016 6 29 06 1075-1084
spelling	10.1007/s11704-016-5132-3 doi (DE-627)SPR021940592 (SPR)s11704-016-5132-3-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Guo, Xiaohui verfasserin aut A probabilistic framework of preference discovery from folksonomy corpus 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered. preference discovery (dpeaa)DE-He213 tagging (dpeaa)DE-He213 folksonomy (dpeaa)DE-He213 social annotation (dpeaa)DE-He213 Hu, Chunming verfasserin aut Zhang, Richong verfasserin aut Huai, Jinpeng verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 11(2016), 6 vom: 29. Juni, Seite 1075-1084 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:11 year:2016 number:6 day:29 month:06 pages:1075-1084 https://dx.doi.org/10.1007/s11704-016-5132-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 11 2016 6 29 06 1075-1084
allfields_unstemmed	10.1007/s11704-016-5132-3 doi (DE-627)SPR021940592 (SPR)s11704-016-5132-3-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Guo, Xiaohui verfasserin aut A probabilistic framework of preference discovery from folksonomy corpus 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered. preference discovery (dpeaa)DE-He213 tagging (dpeaa)DE-He213 folksonomy (dpeaa)DE-He213 social annotation (dpeaa)DE-He213 Hu, Chunming verfasserin aut Zhang, Richong verfasserin aut Huai, Jinpeng verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 11(2016), 6 vom: 29. Juni, Seite 1075-1084 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:11 year:2016 number:6 day:29 month:06 pages:1075-1084 https://dx.doi.org/10.1007/s11704-016-5132-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 11 2016 6 29 06 1075-1084
allfieldsGer	10.1007/s11704-016-5132-3 doi (DE-627)SPR021940592 (SPR)s11704-016-5132-3-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Guo, Xiaohui verfasserin aut A probabilistic framework of preference discovery from folksonomy corpus 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered. preference discovery (dpeaa)DE-He213 tagging (dpeaa)DE-He213 folksonomy (dpeaa)DE-He213 social annotation (dpeaa)DE-He213 Hu, Chunming verfasserin aut Zhang, Richong verfasserin aut Huai, Jinpeng verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 11(2016), 6 vom: 29. Juni, Seite 1075-1084 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:11 year:2016 number:6 day:29 month:06 pages:1075-1084 https://dx.doi.org/10.1007/s11704-016-5132-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 11 2016 6 29 06 1075-1084
allfieldsSound	10.1007/s11704-016-5132-3 doi (DE-627)SPR021940592 (SPR)s11704-016-5132-3-e DE-627 ger DE-627 rakwb eng 004 ASE 54.00 bkl Guo, Xiaohui verfasserin aut A probabilistic framework of preference discovery from folksonomy corpus 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered. preference discovery (dpeaa)DE-He213 tagging (dpeaa)DE-He213 folksonomy (dpeaa)DE-He213 social annotation (dpeaa)DE-He213 Hu, Chunming verfasserin aut Zhang, Richong verfasserin aut Huai, Jinpeng verfasserin aut Enthalten in Frontiers of computer science in China Beijing : Higher Education Press, 2007 11(2016), 6 vom: 29. Juni, Seite 1075-1084 (DE-627)545787726 (DE-600)2388878-7 1673-7466 nnns volume:11 year:2016 number:6 day:29 month:06 pages:1075-1084 https://dx.doi.org/10.1007/s11704-016-5132-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2005 54.00 ASE AR 11 2016 6 29 06 1075-1084
language	English
source	Enthalten in Frontiers of computer science in China 11(2016), 6 vom: 29. Juni, Seite 1075-1084 volume:11 year:2016 number:6 day:29 month:06 pages:1075-1084
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authorswithroles_txt_mv	Guo, Xiaohui @@aut@@ Hu, Chunming @@aut@@ Zhang, Richong @@aut@@ Huai, Jinpeng @@aut@@
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author	Guo, Xiaohui
spellingShingle	Guo, Xiaohui ddc 004 bkl 54.00 misc preference discovery misc tagging misc folksonomy misc social annotation A probabilistic framework of preference discovery from folksonomy corpus
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topic_title	004 ASE 54.00 bkl A probabilistic framework of preference discovery from folksonomy corpus preference discovery (dpeaa)DE-He213 tagging (dpeaa)DE-He213 folksonomy (dpeaa)DE-He213 social annotation (dpeaa)DE-He213
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title	A probabilistic framework of preference discovery from folksonomy corpus
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title_full	A probabilistic framework of preference discovery from folksonomy corpus
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title_sort	probabilistic framework of preference discovery from folksonomy corpus
title_auth	A probabilistic framework of preference discovery from folksonomy corpus
abstract	Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.
abstractGer	Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.
abstract_unstemmed	Abstract The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.
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title_short	A probabilistic framework of preference discovery from folksonomy corpus
url	https://dx.doi.org/10.1007/s11704-016-5132-3
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author2	Hu, Chunming Zhang, Richong Huai, Jinpeng
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up_date	2024-07-04T01:06:47.087Z
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