AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems

Abstract As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and ex...
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Autor*in:	Ma, Xintao [verfasserIn] Dong, Liyan Wang, Yuequn Li, Yongli Zhang, Hao

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Representation learning Graph embedding Knowledge graph Recommendation system

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

Übergeordnetes Werk:	Enthalten in: Knowledge and information systems - London : Springer, 1999, 65(2022), 1 vom: 21. Sept., Seite 163-182
Übergeordnetes Werk:	volume:65 ; year:2022 ; number:1 ; day:21 ; month:09 ; pages:163-182

Links:	Volltext

DOI / URN:	10.1007/s10115-022-01693-6

Katalog-ID:	SPR049067354

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allfields	10.1007/s10115-022-01693-6 doi (DE-627)SPR049067354 (SPR)s10115-022-01693-6-e DE-627 ger DE-627 rakwb eng Ma, Xintao verfasserin aut AKUPP: attention-enhanced joint propagation of knowledge and user preference for 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 As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy. Representation learning (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Dong, Liyan aut Wang, Yuequn aut Li, Yongli aut Zhang, Hao (orcid)0000-0002-2058-7123 aut Enthalten in Knowledge and information systems London : Springer, 1999 65(2022), 1 vom: 21. Sept., Seite 163-182 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:65 year:2022 number:1 day:21 month:09 pages:163-182 https://dx.doi.org/10.1007/s10115-022-01693-6 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_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 65 2022 1 21 09 163-182
spelling	10.1007/s10115-022-01693-6 doi (DE-627)SPR049067354 (SPR)s10115-022-01693-6-e DE-627 ger DE-627 rakwb eng Ma, Xintao verfasserin aut AKUPP: attention-enhanced joint propagation of knowledge and user preference for 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 As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy. Representation learning (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Dong, Liyan aut Wang, Yuequn aut Li, Yongli aut Zhang, Hao (orcid)0000-0002-2058-7123 aut Enthalten in Knowledge and information systems London : Springer, 1999 65(2022), 1 vom: 21. Sept., Seite 163-182 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:65 year:2022 number:1 day:21 month:09 pages:163-182 https://dx.doi.org/10.1007/s10115-022-01693-6 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_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 65 2022 1 21 09 163-182
allfields_unstemmed	10.1007/s10115-022-01693-6 doi (DE-627)SPR049067354 (SPR)s10115-022-01693-6-e DE-627 ger DE-627 rakwb eng Ma, Xintao verfasserin aut AKUPP: attention-enhanced joint propagation of knowledge and user preference for 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 As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy. Representation learning (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Dong, Liyan aut Wang, Yuequn aut Li, Yongli aut Zhang, Hao (orcid)0000-0002-2058-7123 aut Enthalten in Knowledge and information systems London : Springer, 1999 65(2022), 1 vom: 21. Sept., Seite 163-182 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:65 year:2022 number:1 day:21 month:09 pages:163-182 https://dx.doi.org/10.1007/s10115-022-01693-6 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_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 65 2022 1 21 09 163-182
allfieldsGer	10.1007/s10115-022-01693-6 doi (DE-627)SPR049067354 (SPR)s10115-022-01693-6-e DE-627 ger DE-627 rakwb eng Ma, Xintao verfasserin aut AKUPP: attention-enhanced joint propagation of knowledge and user preference for 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 As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy. Representation learning (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Dong, Liyan aut Wang, Yuequn aut Li, Yongli aut Zhang, Hao (orcid)0000-0002-2058-7123 aut Enthalten in Knowledge and information systems London : Springer, 1999 65(2022), 1 vom: 21. Sept., Seite 163-182 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:65 year:2022 number:1 day:21 month:09 pages:163-182 https://dx.doi.org/10.1007/s10115-022-01693-6 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_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 65 2022 1 21 09 163-182
allfieldsSound	10.1007/s10115-022-01693-6 doi (DE-627)SPR049067354 (SPR)s10115-022-01693-6-e DE-627 ger DE-627 rakwb eng Ma, Xintao verfasserin aut AKUPP: attention-enhanced joint propagation of knowledge and user preference for 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 As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy. Representation learning (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Dong, Liyan aut Wang, Yuequn aut Li, Yongli aut Zhang, Hao (orcid)0000-0002-2058-7123 aut Enthalten in Knowledge and information systems London : Springer, 1999 65(2022), 1 vom: 21. Sept., Seite 163-182 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:65 year:2022 number:1 day:21 month:09 pages:163-182 https://dx.doi.org/10.1007/s10115-022-01693-6 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_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 65 2022 1 21 09 163-182
language	English
source	Enthalten in Knowledge and information systems 65(2022), 1 vom: 21. Sept., Seite 163-182 volume:65 year:2022 number:1 day:21 month:09 pages:163-182
sourceStr	Enthalten in Knowledge and information systems 65(2022), 1 vom: 21. Sept., Seite 163-182 volume:65 year:2022 number:1 day:21 month:09 pages:163-182
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Representation learning Graph embedding Knowledge graph Recommendation system
isfreeaccess_bool	false
container_title	Knowledge and information systems
authorswithroles_txt_mv	Ma, Xintao @@aut@@ Dong, Liyan @@aut@@ Wang, Yuequn @@aut@@ Li, Yongli @@aut@@ Zhang, Hao @@aut@@
publishDateDaySort_date	2022-09-21T00:00:00Z
hierarchy_top_id	320627845
id	SPR049067354
language_de	englisch
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author	Ma, Xintao
spellingShingle	Ma, Xintao misc Representation learning misc Graph embedding misc Knowledge graph misc Recommendation system AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems
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topic_title	AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems Representation learning (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213
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topic_unstemmed	misc Representation learning misc Graph embedding misc Knowledge graph misc Recommendation system
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title	AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems
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title_full	AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems
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title_sort	akupp: attention-enhanced joint propagation of knowledge and user preference for recommendation systems
title_auth	AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems
abstract	Abstract As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
abstractGer	Abstract As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
abstract_unstemmed	Abstract As knowledge graphs have attracted enormous attention from researchers, much effort has been invested in recommendation systems to mine user preferences effectively. In particular, knowledge graphs, which convey useful side information about users and items, can provide more accurate and explainable recommendations. When it comes to interactions between entities, however, the majority of existing work fails to incorporate high-order relations that ensure recommendation accuracy. This paper proposes attention-enhanced joint knowledge and user preference propagation (AKUPP), which integrates two types of knowledge propagation. The first is propagating user preferences based on the users' history of interacting items through ripple sets. The second propagation employs an attention mechanism to emphasize the important semantics of relations, and with multiple layers, high-order relations are explored. Therefore, we successfully incorporate both side information and high-order relations in the knowledge graph. We show, via extensive experimentation on real-world datasets, that our approach outperforms numerous state-of-the-art baselines in terms of performance and accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
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title_short	AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems
url	https://dx.doi.org/10.1007/s10115-022-01693-6
remote_bool	true
author2	Dong, Liyan Wang, Yuequn Li, Yongli Zhang, Hao
author2Str	Dong, Liyan Wang, Yuequn Li, Yongli Zhang, Hao
ppnlink	320627845
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s10115-022-01693-6
up_date	2024-07-03T23:07:38.859Z
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AKUPP: attention-enhanced joint propagation of knowledge and user preference for recommendation systems

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