New machine learning model based on the time factor for e-commerce recommendation systems
Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tran, Duy Thanh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 79(2022), 6 vom: 14. Nov., Seite 6756-6801 |
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| Übergeordnetes Werk: |
volume:79 ; year:2022 ; number:6 ; day:14 ; month:11 ; pages:6756-6801 |
| Links: |
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| DOI / URN: |
10.1007/s11227-022-04909-2 |
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SPR049526685 |
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| 520 | |a Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. | ||
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10.1007/s11227-022-04909-2 doi (DE-627)SPR049526685 (SPR)s11227-022-04909-2-e DE-627 ger DE-627 rakwb eng Tran, Duy Thanh verfasserin aut New machine learning model based on the time factor for e-commerce recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. ML.Recommend (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML.NET (dpeaa)DE-He213 Recommendation systems (dpeaa)DE-He213 e-commerce recommendation (dpeaa)DE-He213 e-commerce recommendation systems (dpeaa)DE-He213 Recommender systems (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Huh, Jun-Ho (orcid)0000-0001-6735-6456 aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 6 vom: 14. Nov., Seite 6756-6801 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:6 day:14 month:11 pages:6756-6801 https://dx.doi.org/10.1007/s11227-022-04909-2 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_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 79 2022 6 14 11 6756-6801 |
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10.1007/s11227-022-04909-2 doi (DE-627)SPR049526685 (SPR)s11227-022-04909-2-e DE-627 ger DE-627 rakwb eng Tran, Duy Thanh verfasserin aut New machine learning model based on the time factor for e-commerce recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. ML.Recommend (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML.NET (dpeaa)DE-He213 Recommendation systems (dpeaa)DE-He213 e-commerce recommendation (dpeaa)DE-He213 e-commerce recommendation systems (dpeaa)DE-He213 Recommender systems (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Huh, Jun-Ho (orcid)0000-0001-6735-6456 aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 6 vom: 14. Nov., Seite 6756-6801 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:6 day:14 month:11 pages:6756-6801 https://dx.doi.org/10.1007/s11227-022-04909-2 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_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 79 2022 6 14 11 6756-6801 |
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10.1007/s11227-022-04909-2 doi (DE-627)SPR049526685 (SPR)s11227-022-04909-2-e DE-627 ger DE-627 rakwb eng Tran, Duy Thanh verfasserin aut New machine learning model based on the time factor for e-commerce recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. ML.Recommend (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML.NET (dpeaa)DE-He213 Recommendation systems (dpeaa)DE-He213 e-commerce recommendation (dpeaa)DE-He213 e-commerce recommendation systems (dpeaa)DE-He213 Recommender systems (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Huh, Jun-Ho (orcid)0000-0001-6735-6456 aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 6 vom: 14. Nov., Seite 6756-6801 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:6 day:14 month:11 pages:6756-6801 https://dx.doi.org/10.1007/s11227-022-04909-2 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_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 79 2022 6 14 11 6756-6801 |
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10.1007/s11227-022-04909-2 doi (DE-627)SPR049526685 (SPR)s11227-022-04909-2-e DE-627 ger DE-627 rakwb eng Tran, Duy Thanh verfasserin aut New machine learning model based on the time factor for e-commerce recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. ML.Recommend (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML.NET (dpeaa)DE-He213 Recommendation systems (dpeaa)DE-He213 e-commerce recommendation (dpeaa)DE-He213 e-commerce recommendation systems (dpeaa)DE-He213 Recommender systems (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Huh, Jun-Ho (orcid)0000-0001-6735-6456 aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 6 vom: 14. Nov., Seite 6756-6801 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:6 day:14 month:11 pages:6756-6801 https://dx.doi.org/10.1007/s11227-022-04909-2 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_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 79 2022 6 14 11 6756-6801 |
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10.1007/s11227-022-04909-2 doi (DE-627)SPR049526685 (SPR)s11227-022-04909-2-e DE-627 ger DE-627 rakwb eng Tran, Duy Thanh verfasserin aut New machine learning model based on the time factor for e-commerce recommendation systems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. ML.Recommend (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML.NET (dpeaa)DE-He213 Recommendation systems (dpeaa)DE-He213 e-commerce recommendation (dpeaa)DE-He213 e-commerce recommendation systems (dpeaa)DE-He213 Recommender systems (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Huh, Jun-Ho (orcid)0000-0001-6735-6456 aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 6 vom: 14. Nov., Seite 6756-6801 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:6 day:14 month:11 pages:6756-6801 https://dx.doi.org/10.1007/s11227-022-04909-2 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_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 79 2022 6 14 11 6756-6801 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. 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Tran, Duy Thanh |
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Tran, Duy Thanh misc ML.Recommend misc Machine learning misc ML.NET misc Recommendation systems misc e-commerce recommendation misc e-commerce recommendation systems misc Recommender systems misc Data mining New machine learning model based on the time factor for e-commerce recommendation systems |
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New machine learning model based on the time factor for e-commerce recommendation systems ML.Recommend (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML.NET (dpeaa)DE-He213 Recommendation systems (dpeaa)DE-He213 e-commerce recommendation (dpeaa)DE-He213 e-commerce recommendation systems (dpeaa)DE-He213 Recommender systems (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 |
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new machine learning model based on the time factor for e-commerce recommendation systems |
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New machine learning model based on the time factor for e-commerce recommendation systems |
| abstract |
Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Nowadays, thanks to the development of e-commerce websites, businesses can capitalize on many benefits, for example, there are many methods of approaching customers online. Customers can interact with the product on the system, leave comments or reviews about the product, and capitalize on these interactions helps a lot to reach target customers. Many authors have studied machine learning models to build recommendation systems. The common point of the recommendation system is to filter out the products that are most relevant to the customer in order to retain them longer and to improve the customer’s product experience. In this paper, a new recommendation model called ML.Recommend combined with Microsoft’s ML.NET machine learning platform is proposed. This model provides a full cycle of recommendation modeling, including the steps of preprocessing, model training, model evaluation, model saving and usage. ML.Recommend uses the matrix factor and time factor combination for product recommendations based on ratings and logistic regression for customer comments about products. In this model, we provide a set of interactive class libraries, data, and class models that are implemented based on user evaluations of each interactive product over time. The model recommends corresponding products based on the expected score for the customer that a user has configured. The data are experimented on the e-commerce website called UEL Store and the UCI sentiment labeled sentences dataset. Measurement parameters such as mean absolute error, mean square error, root-mean-square error, R-squared and area under the curve are applied to evaluate the model. Finally, the ML.Recommend model was published on Microsoft’s NuGet system, so that other researchers could use and extend this model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
New machine learning model based on the time factor for e-commerce recommendation systems |
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https://dx.doi.org/10.1007/s11227-022-04909-2 |
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Huh, Jun-Ho |
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10.1007/s11227-022-04909-2 |
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2024-07-04T01:10:59.020Z |
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| score |
7.398514 |