Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems

Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discriminati...
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Autor*in:	Liu, Haifeng [verfasserIn] Wang, Yukai Lin, Hongfei Xu, Bo Zhao, Nan

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Recommendation systems Fairness Gender bias Adversarial learning

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 34(2022), 20 vom: 11. Juni, Seite 18097-18111
Übergeordnetes Werk:	volume:34 ; year:2022 ; number:20 ; day:11 ; month:06 ; pages:18097-18111

Links:	Volltext

DOI / URN:	10.1007/s00521-022-07373-4

Katalog-ID:	SPR048182893

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520			\|a Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation.
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allfields	10.1007/s00521-022-07373-4 doi (DE-627)SPR048182893 (SPR)s00521-022-07373-4-e DE-627 ger DE-627 rakwb eng Liu, Haifeng verfasserin aut Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation. Recommendation systems (dpeaa)DE-He213 Fairness (dpeaa)DE-He213 Gender bias (dpeaa)DE-He213 Adversarial learning (dpeaa)DE-He213 Wang, Yukai aut Lin, Hongfei aut Xu, Bo aut Zhao, Nan aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 20 vom: 11. Juni, Seite 18097-18111 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:20 day:11 month:06 pages:18097-18111 https://dx.doi.org/10.1007/s00521-022-07373-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 20 11 06 18097-18111
spelling	10.1007/s00521-022-07373-4 doi (DE-627)SPR048182893 (SPR)s00521-022-07373-4-e DE-627 ger DE-627 rakwb eng Liu, Haifeng verfasserin aut Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation. Recommendation systems (dpeaa)DE-He213 Fairness (dpeaa)DE-He213 Gender bias (dpeaa)DE-He213 Adversarial learning (dpeaa)DE-He213 Wang, Yukai aut Lin, Hongfei aut Xu, Bo aut Zhao, Nan aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 20 vom: 11. Juni, Seite 18097-18111 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:20 day:11 month:06 pages:18097-18111 https://dx.doi.org/10.1007/s00521-022-07373-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 20 11 06 18097-18111
allfields_unstemmed	10.1007/s00521-022-07373-4 doi (DE-627)SPR048182893 (SPR)s00521-022-07373-4-e DE-627 ger DE-627 rakwb eng Liu, Haifeng verfasserin aut Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation. Recommendation systems (dpeaa)DE-He213 Fairness (dpeaa)DE-He213 Gender bias (dpeaa)DE-He213 Adversarial learning (dpeaa)DE-He213 Wang, Yukai aut Lin, Hongfei aut Xu, Bo aut Zhao, Nan aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 20 vom: 11. Juni, Seite 18097-18111 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:20 day:11 month:06 pages:18097-18111 https://dx.doi.org/10.1007/s00521-022-07373-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 20 11 06 18097-18111
allfieldsGer	10.1007/s00521-022-07373-4 doi (DE-627)SPR048182893 (SPR)s00521-022-07373-4-e DE-627 ger DE-627 rakwb eng Liu, Haifeng verfasserin aut Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation. Recommendation systems (dpeaa)DE-He213 Fairness (dpeaa)DE-He213 Gender bias (dpeaa)DE-He213 Adversarial learning (dpeaa)DE-He213 Wang, Yukai aut Lin, Hongfei aut Xu, Bo aut Zhao, Nan aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 20 vom: 11. Juni, Seite 18097-18111 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:20 day:11 month:06 pages:18097-18111 https://dx.doi.org/10.1007/s00521-022-07373-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 20 11 06 18097-18111
allfieldsSound	10.1007/s00521-022-07373-4 doi (DE-627)SPR048182893 (SPR)s00521-022-07373-4-e DE-627 ger DE-627 rakwb eng Liu, Haifeng verfasserin aut Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation. Recommendation systems (dpeaa)DE-He213 Fairness (dpeaa)DE-He213 Gender bias (dpeaa)DE-He213 Adversarial learning (dpeaa)DE-He213 Wang, Yukai aut Lin, Hongfei aut Xu, Bo aut Zhao, Nan aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 20 vom: 11. Juni, Seite 18097-18111 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:20 day:11 month:06 pages:18097-18111 https://dx.doi.org/10.1007/s00521-022-07373-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 20 11 06 18097-18111
language	English
source	Enthalten in Neural computing & applications 34(2022), 20 vom: 11. Juni, Seite 18097-18111 volume:34 year:2022 number:20 day:11 month:06 pages:18097-18111
sourceStr	Enthalten in Neural computing & applications 34(2022), 20 vom: 11. Juni, Seite 18097-18111 volume:34 year:2022 number:20 day:11 month:06 pages:18097-18111
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Recommendation systems Fairness Gender bias Adversarial learning
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Liu, Haifeng @@aut@@ Wang, Yukai @@aut@@ Lin, Hongfei @@aut@@ Xu, Bo @@aut@@ Zhao, Nan @@aut@@
publishDateDaySort_date	2022-06-11T00:00:00Z
hierarchy_top_id	271595574
id	SPR048182893
language_de	englisch
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author	Liu, Haifeng
spellingShingle	Liu, Haifeng misc Recommendation systems misc Fairness misc Gender bias misc Adversarial learning Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems
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topic_title	Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems Recommendation systems (dpeaa)DE-He213 Fairness (dpeaa)DE-He213 Gender bias (dpeaa)DE-He213 Adversarial learning (dpeaa)DE-He213
topic	misc Recommendation systems misc Fairness misc Gender bias misc Adversarial learning
topic_unstemmed	misc Recommendation systems misc Fairness misc Gender bias misc Adversarial learning
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title	Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems
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title_full	Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems
author_sort	Liu, Haifeng
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author_browse	Liu, Haifeng Wang, Yukai Lin, Hongfei Xu, Bo Zhao, Nan
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doi_str_mv	10.1007/s00521-022-07373-4
title_sort	mitigating sensitive data exposure with adversarial learning for fairness recommendation systems
title_auth	Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems
abstract	Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
abstractGer	Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
abstract_unstemmed	Abstract Fairness is an important research problem for recommendation systems, and unfair recommendation methods can lead to discrimination against users. Gender is a kind of sensitive feature, exposure sensitive feature can lead to unfair treatment of males and females. However, gender discrimination is still a significant challenge that cannot be addressed well with current recommender methods. To alleviate this problem, we present a novel Unbias Gender Recommendation (UGRec) to balance performance between females and males. We propose a multihop graph aggregation mechanism to improve the representation of users and items, and we design a gender debias component with adversarial learning to eliminate gender bias by filtering out users’ representations. With this design, UGRec can effectively filter out gender bias information and balance fairness between females and males. The experimental results based on three datasets demonstrate the effectiveness and advancement of our proposed UGRec model on fair recommendation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
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title_short	Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems
url	https://dx.doi.org/10.1007/s00521-022-07373-4
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doi_str	10.1007/s00521-022-07373-4
up_date	2024-07-03T17:34:12.292Z
_version_	1803580134964330496
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