Presentation 2013-07-18
An improvement of a Neutrality Term in an Information-neutral Recommender System
Toshihiro KAMISHIMA, Shotaro AKAHO, Hideki ASOH, Jun SAKUMA,
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Abstract(in English) Information-neutral recommender systems aim to make recommendations whose neutrality from the specified viewpoint is guaranteed. Such systems is developed for dissolving a filter bubble problem, which is the bias or restriction that provided to people by the influence of personalization technologies. Our previously developed system was not scalable because efficient optimization techniques could not be applied. To address this problem, we developed a penalty term to guarantee the neutrality that can be analytically differentiable.
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Keyword(in English) fairness-aware data mining / recommender system / matrix factorization / neutrality / filter bubble
Paper # IBISML2013-7
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Conference Information
Committee IBISML
Conference Date 2013/7/11(1days)
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Paper Information
Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An improvement of a Neutrality Term in an Information-neutral Recommender System
Sub Title (in English)
Keyword(1) fairness-aware data mining
Keyword(2) recommender system
Keyword(3) matrix factorization
Keyword(4) neutrality
Keyword(5) filter bubble
1st Author's Name Toshihiro KAMISHIMA
1st Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)()
2nd Author's Name Shotaro AKAHO
2nd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
3rd Author's Name Hideki ASOH
3rd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
4th Author's Name Jun SAKUMA
4th Author's Affiliation University of Tsukuba
Date 2013-07-18
Paper # IBISML2013-7
Volume (vol) vol.113
Number (no) 139
Page pp.pp.-
#Pages 8
Date of Issue