Product collaborative filtering based recommendation systems for large-scale E-commerce
•E-commerce demands multi-choice products, challenging businesses.•Recommender systems reshape E-commerce with personalized experiences.•Scalability is a pressing issue for recommendation systems.•Parallel techniques tackle scalability challenges in E-commerce.•Apache Spark accelerates training time...
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| Veröffentlicht in: | International journal of information management data insights Jg. 5; H. 1; S. 100322 |
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| Sprache: | Englisch |
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01.06.2025
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| Abstract | •E-commerce demands multi-choice products, challenging businesses.•Recommender systems reshape E-commerce with personalized experiences.•Scalability is a pressing issue for recommendation systems.•Parallel techniques tackle scalability challenges in E-commerce.•Apache Spark accelerates training time for large-scale E-commerce.
The rapid growth in e-commerce and the increasing diversity of customer preferences necessitates the development of an effective recommender system for a business offering a wide range of products. This paper introduces a product-based collaborative filtering approach utilizing Apache Spark, a powerful parallel processing framework to address the scalability issues of recommender systems in the cloud computing environment. Using Spark's distributed computing ability, our model attains a surprising 7.6 times speedup on the training time compared to traditional single-machine methods while preserving accuracy with a Root Mean Square Error (RMSE) 0.9. These results demonstrate the effectiveness of parallel and distributed techniques in developing efficient and accurate recommender systems for large-scale e-commerce applications. Future work will focus on applying multi-model to enhance the accuracy of prediction and configuration to optimize the cost of cluster operations. |
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| AbstractList | •E-commerce demands multi-choice products, challenging businesses.•Recommender systems reshape E-commerce with personalized experiences.•Scalability is a pressing issue for recommendation systems.•Parallel techniques tackle scalability challenges in E-commerce.•Apache Spark accelerates training time for large-scale E-commerce.
The rapid growth in e-commerce and the increasing diversity of customer preferences necessitates the development of an effective recommender system for a business offering a wide range of products. This paper introduces a product-based collaborative filtering approach utilizing Apache Spark, a powerful parallel processing framework to address the scalability issues of recommender systems in the cloud computing environment. Using Spark's distributed computing ability, our model attains a surprising 7.6 times speedup on the training time compared to traditional single-machine methods while preserving accuracy with a Root Mean Square Error (RMSE) 0.9. These results demonstrate the effectiveness of parallel and distributed techniques in developing efficient and accurate recommender systems for large-scale e-commerce applications. Future work will focus on applying multi-model to enhance the accuracy of prediction and configuration to optimize the cost of cluster operations. The rapid growth in e-commerce and the increasing diversity of customer preferences necessitates the development of an effective recommender system for a business offering a wide range of products. This paper introduces a product-based collaborative filtering approach utilizing Apache Spark, a powerful parallel processing framework to address the scalability issues of recommender systems in the cloud computing environment. Using Spark's distributed computing ability, our model attains a surprising 7.6 times speedup on the training time compared to traditional single-machine methods while preserving accuracy with a Root Mean Square Error (RMSE) 0.9. These results demonstrate the effectiveness of parallel and distributed techniques in developing efficient and accurate recommender systems for large-scale e-commerce applications. Future work will focus on applying multi-model to enhance the accuracy of prediction and configuration to optimize the cost of cluster operations. |
| ArticleNumber | 100322 |
| Author | Nguyen, Van-Ho Trinh, Trang Nguyen, Nghia Nguyen, Duy-Nghia |
| Author_xml | – sequence: 1 givenname: Trang surname: Trinh fullname: Trinh, Trang organization: University of Economics and Law, Ho Chi Minh City, Vietnam – sequence: 2 givenname: Van-Ho orcidid: 0000-0001-6706-0276 surname: Nguyen fullname: Nguyen, Van-Ho email: honv@uel.edu.vn organization: University of Economics and Law, Ho Chi Minh City, Vietnam – sequence: 3 givenname: Nghia surname: Nguyen fullname: Nguyen, Nghia organization: University of Economics and Law, Ho Chi Minh City, Vietnam – sequence: 4 givenname: Duy-Nghia surname: Nguyen fullname: Nguyen, Duy-Nghia organization: University of Economics and Law, Ho Chi Minh City, Vietnam |
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| Cites_doi | 10.6007/IJARAFMS/v11-i1/8987 10.1016/j.ins.2009.06.004 10.14778/2367502.2367562 10.1016/j.jjimei.2022.100145 10.1080/03772063.2021.1997357 10.25124/jmi.v19i3.2412 10.1109/ACCESS.2019.2937518 10.1504/IJCSE.2020.105727 10.1093/humupd/dmac035 10.1007/s10639-019-10063-9 10.1108/K-03-2019-0199 10.1016/j.jretconser.2020.102287 10.1515/cait-2016-0092 10.1109/ACCESS.2020.3002803 10.5194/gmd-15-5481-2022 10.1016/j.jjimei.2024.100256 10.1016/j.jjimei.2021.100027 10.1016/j.eij.2015.06.005 10.1002/cb.1588 10.1109/MC.2009.263 10.1016/j.jesp.2013.03.013 10.1016/j.jjimei.2022.100090 10.1016/j.knosys.2009.07.007 10.1016/j.jjimei.2022.100139 10.1007/s40031-024-00999-z 10.1016/j.joitmc.2024.100303 10.1016/j.knosys.2019.105058 |
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| Keywords | Recommendation systems E-commerce Parallel and distributed computing Collaborative filtering Apache spark Large-scale |
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| References | Ferreira, Silva, Abelha, Machado (bib0007) 2020 Whinston, Choi, Stahl (bib0046) 1997 Dhawan, Singh, Batra (bib48) 2024; 105 Tran (bib0038) 2021; 58 Isinkaye (bib0014) 2023; 69 Rastogi, Jothi, Mishra (bib0030) 2022 Dittrich, Quiané-Ruiz (bib0005) 2012; 5 Koren, Bell, Volinsky (bib0016) 2009; 42 Negre (bib0025) 2015 Behera, Nain (bib0004) 2022 Gosh, Nahar, Wahab, Biswas, Hossain, Andersson (bib0010) 2020 Yan, Zhang, Li, Wu, Sun, Wang, Chen (bib0045) 2016; 15 Alzogbi, Koleva, Lausen (bib0002) 2019 Wilson (bib0042) 2019; 19 Mehta, Rana (bib0027) 2017 Ni, Li, McAuley (bib0024) 2019 Mutemi, Bacao (bib0023) 2024; 4 Rajendran, Sundarraj (bib0028) 2021; 1 RaviKanth, ChandraShekar, Sreekanth, Kumar (bib0031) 2021 Schafer, Konstan, Riedl (bib0033) 1999 Wakil, Alyari, Ghasvari, Lesani, Rajabion (bib0040) 2020; 49 Khanal, Prasad, Alsadoon, Maag (bib0015) 2020; 25 Leys, Ley, Klein, Bernard, Licata (bib0018) 2013; 49 Levine, Jørgensen, Martino-Andrade, Mendiola, Weksler-Derri, Jolles, Pinotti, Swan (bib0019) 2022; 29 Alamdari, Navimipour, Hosseinzadeh, Safaei, Darwesh (bib0003) 2020; 8 Venkatachalam, Ray (bib0039) 2022; 2 (bib0001) 2017 Gu, Ding, Wang, Zou, Liu, Yin (bib0011) 2020 Nguyen, Nguyen, Trinh, Ho, Le (bib0006) 2024; 10 Porcel, Herrera-Viedma (bib0026) 2010; 23 Shen, Zhou, Chen (bib0035) 2020; 21 Sivapalan, Sadeghian, Rahnama, Madni (bib47) 2014 Xie, Zhou, Li (bib0043) 2016; 16 Shandilya, Sharma, Wong (bib0034) 2022; 2 Rashid (bib0029) 2002 Hodson (bib0012) 2022; 15 Taher (bib0037) 2021; 11 Wei, Huang, Fu (bib0041) 2007 Salunke, Nichite (bib0032) 2022 Gazdar, Hidri (bib0009) 2020; 188 Isinkaye, Folajimi, Ojokoh (bib0013) 2015; 16 Liu, Lai, Lee (bib0020) 2009; 179 Mishra, Jothi, Urolagin, Irani (bib0022) 2023; 3 Falk (bib0008) 2019 Shoja, Tabrizi (bib0036) 2019; 7 MacKenzie, Meyer, Noble (bib0021) 2013 Isinkaye (10.1016/j.jjimei.2025.100322_bib0013) 2015; 16 Whinston (10.1016/j.jjimei.2025.100322_bib0046) 1997 Dhawan (10.1016/j.jjimei.2025.100322_bib48) 2024; 105 Levine (10.1016/j.jjimei.2025.100322_bib0019) 2022; 29 Mutemi (10.1016/j.jjimei.2025.100322_bib0023) 2024; 4 Xie (10.1016/j.jjimei.2025.100322_bib0043) 2016; 16 Negre (10.1016/j.jjimei.2025.100322_bib0025) 2015 Rajendran (10.1016/j.jjimei.2025.100322_bib0028) 2021; 1 Alzogbi (10.1016/j.jjimei.2025.100322_bib0002) 2019 Yan (10.1016/j.jjimei.2025.100322_bib0045) 2016; 15 Nguyen (10.1016/j.jjimei.2025.100322_bib0006) 2024; 10 RaviKanth (10.1016/j.jjimei.2025.100322_bib0031) 2021 Sivapalan (10.1016/j.jjimei.2025.100322_bib47) 2014 Shen (10.1016/j.jjimei.2025.100322_bib0035) 2020; 21 Falk (10.1016/j.jjimei.2025.100322_bib0008) 2019 Gosh (10.1016/j.jjimei.2025.100322_bib0010) 2020 Ni (10.1016/j.jjimei.2025.100322_bib0024) 2019 Liu (10.1016/j.jjimei.2025.100322_bib0020) 2009; 179 Shandilya (10.1016/j.jjimei.2025.100322_bib0034) 2022; 2 Dittrich (10.1016/j.jjimei.2025.100322_bib0005) 2012; 5 Shoja (10.1016/j.jjimei.2025.100322_bib0036) 2019; 7 Taher (10.1016/j.jjimei.2025.100322_bib0037) 2021; 11 Rashid (10.1016/j.jjimei.2025.100322_bib0029) 2002 Wei (10.1016/j.jjimei.2025.100322_bib0041) 2007 Wilson (10.1016/j.jjimei.2025.100322_bib0042) 2019; 19 Salunke (10.1016/j.jjimei.2025.100322_bib0032) 2022 Wakil (10.1016/j.jjimei.2025.100322_bib0040) 2020; 49 Tran (10.1016/j.jjimei.2025.100322_bib0038) 2021; 58 Gazdar (10.1016/j.jjimei.2025.100322_bib0009) 2020; 188 Hodson (10.1016/j.jjimei.2025.100322_bib0012) 2022; 15 Gu (10.1016/j.jjimei.2025.100322_bib0011) 2020 Porcel (10.1016/j.jjimei.2025.100322_bib0026) 2010; 23 Alamdari (10.1016/j.jjimei.2025.100322_bib0003) 2020; 8 Koren (10.1016/j.jjimei.2025.100322_bib0016) 2009; 42 Rastogi (10.1016/j.jjimei.2025.100322_bib0030) 2022 Schafer (10.1016/j.jjimei.2025.100322_bib0033) 1999 Behera (10.1016/j.jjimei.2025.100322_bib0004) 2022 MacKenzie (10.1016/j.jjimei.2025.100322_bib0021) 2013 Mehta (10.1016/j.jjimei.2025.100322_bib0027) 2017 Khanal (10.1016/j.jjimei.2025.100322_bib0015) 2020; 25 Mishra (10.1016/j.jjimei.2025.100322_bib0022) 2023; 3 (10.1016/j.jjimei.2025.100322_bib0001) 2017 Venkatachalam (10.1016/j.jjimei.2025.100322_bib0039) 2022; 2 Ferreira (10.1016/j.jjimei.2025.100322_bib0007) 2020 Isinkaye (10.1016/j.jjimei.2025.100322_bib0014) 2023; 69 Leys (10.1016/j.jjimei.2025.100322_bib0018) 2013; 49 |
| References_xml | – year: 2007 ident: bib0041 article-title: A survey of E-commerce recommender systems publication-title: Proceedings - ICSSSM’07: 2007 International Conference on Service Systems and Service Management – volume: 69 start-page: 6087 year: 2023 end-page: 6100 ident: bib0014 article-title: Matrix factorization in recommender systems: Algorithms, applications, and peculiar challenges publication-title: IETE Journal of Research – volume: 29 start-page: 157 year: 2022 end-page: 176 ident: bib0019 article-title: Temporal trends in sperm count: A systematic review and meta-regression analysis of samples collected globally in the 20th and 21st centuries publication-title: Human Reproduction Update – volume: 2 year: 2022 ident: bib0034 article-title: MATURE-food: Food recommender system for mandatory feature choices A system for enabling digital health publication-title: International Journal of Information Management Data Insights – start-page: 193 year: 2019 end-page: 200 ident: bib0002 article-title: Towards distributed multi-model learning on Apache Spark for model-based recommender publication-title: 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW) – volume: 49 start-page: 764 year: 2013 end-page: 766 ident: bib0018 article-title: Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median publication-title: Journal of Experimental Social Psychology – volume: 8 start-page: 115694 year: 2020 end-page: 115716 ident: bib0003 article-title: A systematic study on the recommender systems in the E-commerce publication-title: IEEE access : practical innovations, open solutions – start-page: 127 year: 2002 end-page: 134 ident: bib0029 article-title: Getting to know you: Learning new user preferences in recommender systems publication-title: Proceedings of the international conference on intelligent user interfaces – volume: 23 start-page: 32 year: 2010 end-page: 39 ident: bib0026 article-title: Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries publication-title: Knowledge-Based Systems – year: 2022 ident: bib0032 article-title: Recommender systems in E-commerce – volume: 42 start-page: 30 year: 2009 end-page: 37 ident: bib0016 article-title: Matrix factorization techniques for recommender systems publication-title: Computer – volume: 16 start-page: 245 year: 2016 end-page: 255 ident: bib0043 article-title: Application of improved recommendation system based on SPARK platform in big data analysis publication-title: Cybernetics and Information Technologies – volume: 1 year: 2021 ident: bib0028 article-title: Using topic models with browsing history in hybrid collaborative filtering recommender system: Experiments with user ratings publication-title: International Journal of Information Management Data Insights – volume: 49 start-page: 1325 year: 2020 end-page: 1346 ident: bib0040 article-title: A new model for assessing the role of customer behavior history, product classification, and prices on the success of the recommender systems in e-commerce publication-title: Kybernetes – start-page: 158 year: 1999 end-page: 166 ident: bib0033 article-title: Recommender systems in e-commerce publication-title: Proceedings of the 1st ACM conference on Electronic commerce – volume: 11 start-page: 153 year: 2021 end-page: 165 ident: bib0037 article-title: E-commerce: Advantages and limitations publication-title: International Journal of Academic Research in Accounting Finance and Management Sciences – volume: 7 start-page: 119121 year: 2019 end-page: 119130 ident: bib0036 article-title: Customer reviews analysis with deep neural networks for E-Commerce Recommender systems publication-title: IEEE Access – year: 1997 ident: bib0046 article-title: The economics of electronic commerce – start-page: 137 year: 2022 end-page: 146 ident: bib0004 article-title: Trade-off between memory and model-based collaborative filtering recommender system publication-title: Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences: PCCDS 2021 – year: 2017 ident: bib0001 publication-title: Proceedings of the Eleventh ACM Conference on Recommender Systems – year: 2013 ident: bib0021 article-title: How retailers can keep up with consumers – start-page: 2493 year: 2020 end-page: 2500 ident: bib0011 article-title: Deep multifaceted transformers for multi-objective ranking in large-scale e-commerce recommender systems publication-title: Proceedings of the 29th ACM International Conference on Information & Knowledge Management – volume: 21 start-page: 219 year: 2020 end-page: 225 ident: bib0035 article-title: Collaborative filtering-based recommendation system for big data publication-title: International Journal of Computational Science and Engineering – volume: 15 start-page: 516 year: 2016 end-page: 526 ident: bib0045 article-title: Effects of product portfolios and recommendation timing in the efficiency of personalized recommendation publication-title: J. Consumer Behav. – volume: 25 start-page: 2635 year: 2020 end-page: 2664 ident: bib0015 article-title: A systematic review: Machine learning based recommendation systems for e-learning publication-title: Education and Information Technologies – volume: 10 start-page: 100303 year: 2024 ident: bib0006 article-title: A personalized product recommendation model in e-commerce based on retrieval strategy publication-title: Journal of Open Innovation: Technology, Market, and Complexity – volume: 4 year: 2024 ident: bib0023 article-title: Balancing act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models publication-title: International Journal of Information Management Data Insights – start-page: 269 year: 2017 end-page: 274 ident: bib0027 article-title: A review on matrix factorization techniques in recommender systems publication-title: 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA) – volume: 105 start-page: 657 year: 2024 end-page: 675 ident: bib48 article-title: A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media publication-title: J. Inst. Eng. India Ser. B – start-page: 188 year: 2019 end-page: 197 ident: bib0024 article-title: Justifying recommendations using distantly-labeled reviews and fine-grained aspects publication-title: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) – volume: 58 year: 2021 ident: bib0038 article-title: Managing the effectiveness of e-commerce platforms in a pandemic publication-title: Journal of Retailing and Consumer Services – volume: 188 year: 2020 ident: bib0009 article-title: A new similarity measure for collaborative filtering-based Recommender Systems publication-title: Knowledge-Based Systems – volume: 15 start-page: 5481 year: 2022 end-page: 5487 ident: bib0012 article-title: Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not publication-title: Geoscientific Model Development – start-page: 880 year: 2020 end-page: 893 ident: bib0010 article-title: Recommendation system for e-commerce using alternating least squares (ALS) on apache spark publication-title: International Conference on Intelligent Computing & Optimization – volume: 5 start-page: 2014 year: 2012 end-page: 2015 ident: bib0005 article-title: Efficient big data processing in Hadoop MapReduce publication-title: Proceedings of the VLDB Endowment – volume: 179 start-page: 3505 year: 2009 end-page: 3519 ident: bib0020 article-title: A hybrid of sequential rules and collaborative filtering for product recommendation publication-title: Information Sciences – start-page: 123 year: 2021 end-page: 129 ident: bib0031 article-title: Recommendation system for e-commerce by memory based and model based collaborative filtering publication-title: Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) – start-page: 179 year: 2014 end-page: 184 ident: bib47 article-title: August). Recommender systems in e-commerce – year: 2019 ident: bib0008 article-title: Practical recommender systems – volume: 3 year: 2023 ident: bib0022 article-title: Knowledge based topic retrieval for recommendations and tourism promotions publication-title: International Journal of Information Management Data Insights – year: 2015 ident: bib0025 article-title: Chapter 5 evaluating the quality of recommender systems publication-title: . essay – start-page: 514 year: 2022 end-page: 519 ident: bib0030 article-title: Tourist spot recommendation from images based on age group and location for Dubai using Deep Transfer learning publication-title: 2022 8th International Conference on Signal Processing and Communication (ICSC) – volume: 16 start-page: 261 year: 2015 end-page: 273 ident: bib0013 article-title: Recommendation systems: Principles, methods and evaluation publication-title: Egyptian Informatics Journal – volume: 19 start-page: 241 year: 2019 end-page: 249 ident: bib0042 article-title: The impact of perceived usefulness and perceived ease-of-use toward repurchase intention In The Indonesian E-commerce industry publication-title: Jurnal Manajemen Indonesia – year: 2020 ident: bib0007 article-title: Recommendation system -using autoencoders publication-title: MDPI – volume: 2 year: 2022 ident: bib0039 article-title: How do context-aware artificial intelligence algorithms used in fitness recommender systems? A literature review and research agenda publication-title: International Journal of Information Management Data Insights – volume: 11 start-page: 153 issue: 1 year: 2021 ident: 10.1016/j.jjimei.2025.100322_bib0037 article-title: E-commerce: Advantages and limitations publication-title: International Journal of Academic Research in Accounting Finance and Management Sciences doi: 10.6007/IJARAFMS/v11-i1/8987 – volume: 179 start-page: 3505 issue: 20 year: 2009 ident: 10.1016/j.jjimei.2025.100322_bib0020 article-title: A hybrid of sequential rules and collaborative filtering for product recommendation publication-title: Information Sciences doi: 10.1016/j.ins.2009.06.004 – volume: 5 start-page: 2014 issue: 12 year: 2012 ident: 10.1016/j.jjimei.2025.100322_bib0005 article-title: Efficient big data processing in Hadoop MapReduce publication-title: Proceedings of the VLDB Endowment doi: 10.14778/2367502.2367562 – start-page: 158 year: 1999 ident: 10.1016/j.jjimei.2025.100322_bib0033 article-title: Recommender systems in e-commerce – year: 2017 ident: 10.1016/j.jjimei.2025.100322_bib0001 – volume: 3 issue: 1 year: 2023 ident: 10.1016/j.jjimei.2025.100322_bib0022 article-title: Knowledge based topic retrieval for recommendations and tourism promotions publication-title: International Journal of Information Management Data Insights doi: 10.1016/j.jjimei.2022.100145 – volume: 69 start-page: 6087 issue: 9 year: 2023 ident: 10.1016/j.jjimei.2025.100322_bib0014 article-title: Matrix factorization in recommender systems: Algorithms, applications, and peculiar challenges publication-title: IETE Journal of Research doi: 10.1080/03772063.2021.1997357 – volume: 19 start-page: 241 issue: 3 year: 2019 ident: 10.1016/j.jjimei.2025.100322_bib0042 article-title: The impact of perceived usefulness and perceived ease-of-use toward repurchase intention In The Indonesian E-commerce industry publication-title: Jurnal Manajemen Indonesia doi: 10.25124/jmi.v19i3.2412 – start-page: 137 year: 2022 ident: 10.1016/j.jjimei.2025.100322_bib0004 article-title: Trade-off between memory and model-based collaborative filtering recommender system – start-page: 188 year: 2019 ident: 10.1016/j.jjimei.2025.100322_bib0024 article-title: Justifying recommendations using distantly-labeled reviews and fine-grained aspects – volume: 7 start-page: 119121 year: 2019 ident: 10.1016/j.jjimei.2025.100322_bib0036 article-title: Customer reviews analysis with deep neural networks for E-Commerce Recommender systems publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2937518 – volume: 21 start-page: 219 issue: 2 year: 2020 ident: 10.1016/j.jjimei.2025.100322_bib0035 article-title: Collaborative filtering-based recommendation system for big data publication-title: International Journal of Computational Science and Engineering doi: 10.1504/IJCSE.2020.105727 – start-page: 179 year: 2014 ident: 10.1016/j.jjimei.2025.100322_bib47 article-title: August). Recommender systems in e-commerce – year: 2022 ident: 10.1016/j.jjimei.2025.100322_bib0032 – volume: 29 start-page: 157 issue: 2 year: 2022 ident: 10.1016/j.jjimei.2025.100322_bib0019 article-title: Temporal trends in sperm count: A systematic review and meta-regression analysis of samples collected globally in the 20th and 21st centuries publication-title: Human Reproduction Update doi: 10.1093/humupd/dmac035 – volume: 25 start-page: 2635 issue: 4 year: 2020 ident: 10.1016/j.jjimei.2025.100322_bib0015 article-title: A systematic review: Machine learning based recommendation systems for e-learning publication-title: Education and Information Technologies doi: 10.1007/s10639-019-10063-9 – volume: 49 start-page: 1325 issue: 5 year: 2020 ident: 10.1016/j.jjimei.2025.100322_bib0040 article-title: A new model for assessing the role of customer behavior history, product classification, and prices on the success of the recommender systems in e-commerce publication-title: Kybernetes doi: 10.1108/K-03-2019-0199 – volume: 58 year: 2021 ident: 10.1016/j.jjimei.2025.100322_bib0038 article-title: Managing the effectiveness of e-commerce platforms in a pandemic publication-title: Journal of Retailing and Consumer Services doi: 10.1016/j.jretconser.2020.102287 – year: 2013 ident: 10.1016/j.jjimei.2025.100322_bib0021 – start-page: 514 year: 2022 ident: 10.1016/j.jjimei.2025.100322_bib0030 article-title: Tourist spot recommendation from images based on age group and location for Dubai using Deep Transfer learning – start-page: 269 year: 2017 ident: 10.1016/j.jjimei.2025.100322_bib0027 article-title: A review on matrix factorization techniques in recommender systems – volume: 16 start-page: 245 issue: 6 year: 2016 ident: 10.1016/j.jjimei.2025.100322_bib0043 article-title: Application of improved recommendation system based on SPARK platform in big data analysis publication-title: Cybernetics and Information Technologies doi: 10.1515/cait-2016-0092 – volume: 8 start-page: 115694 year: 2020 ident: 10.1016/j.jjimei.2025.100322_bib0003 article-title: A systematic study on the recommender systems in the E-commerce publication-title: IEEE access : practical innovations, open solutions doi: 10.1109/ACCESS.2020.3002803 – volume: 15 start-page: 5481 issue: 14 year: 2022 ident: 10.1016/j.jjimei.2025.100322_bib0012 article-title: Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not publication-title: Geoscientific Model Development doi: 10.5194/gmd-15-5481-2022 – start-page: 193 year: 2019 ident: 10.1016/j.jjimei.2025.100322_bib0002 article-title: Towards distributed multi-model learning on Apache Spark for model-based recommender – volume: 4 issue: 2 year: 2024 ident: 10.1016/j.jjimei.2025.100322_bib0023 article-title: Balancing act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models publication-title: International Journal of Information Management Data Insights doi: 10.1016/j.jjimei.2024.100256 – volume: 1 issue: 2 year: 2021 ident: 10.1016/j.jjimei.2025.100322_bib0028 article-title: Using topic models with browsing history in hybrid collaborative filtering recommender system: Experiments with user ratings publication-title: International Journal of Information Management Data Insights doi: 10.1016/j.jjimei.2021.100027 – volume: 16 start-page: 261 issue: 3 year: 2015 ident: 10.1016/j.jjimei.2025.100322_bib0013 article-title: Recommendation systems: Principles, methods and evaluation publication-title: Egyptian Informatics Journal doi: 10.1016/j.eij.2015.06.005 – year: 2020 ident: 10.1016/j.jjimei.2025.100322_bib0007 article-title: Recommendation system -using autoencoders publication-title: MDPI – year: 2007 ident: 10.1016/j.jjimei.2025.100322_bib0041 article-title: A survey of E-commerce recommender systems – volume: 15 start-page: 516 year: 2016 ident: 10.1016/j.jjimei.2025.100322_bib0045 article-title: Effects of product portfolios and recommendation timing in the efficiency of personalized recommendation publication-title: J. Consumer Behav. doi: 10.1002/cb.1588 – start-page: 880 year: 2020 ident: 10.1016/j.jjimei.2025.100322_bib0010 article-title: Recommendation system for e-commerce using alternating least squares (ALS) on apache spark – volume: 42 start-page: 30 issue: 8 year: 2009 ident: 10.1016/j.jjimei.2025.100322_bib0016 article-title: Matrix factorization techniques for recommender systems publication-title: Computer doi: 10.1109/MC.2009.263 – volume: 49 start-page: 764 issue: 4 year: 2013 ident: 10.1016/j.jjimei.2025.100322_bib0018 article-title: Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median publication-title: Journal of Experimental Social Psychology doi: 10.1016/j.jesp.2013.03.013 – start-page: 127 year: 2002 ident: 10.1016/j.jjimei.2025.100322_bib0029 article-title: Getting to know you: Learning new user preferences in recommender systems – volume: 2 issue: 2 year: 2022 ident: 10.1016/j.jjimei.2025.100322_bib0034 article-title: MATURE-food: Food recommender system for mandatory feature choices A system for enabling digital health publication-title: International Journal of Information Management Data Insights doi: 10.1016/j.jjimei.2022.100090 – volume: 23 start-page: 32 issue: 1 year: 2010 ident: 10.1016/j.jjimei.2025.100322_bib0026 article-title: Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2009.07.007 – start-page: 123 year: 2021 ident: 10.1016/j.jjimei.2025.100322_bib0031 article-title: Recommendation system for e-commerce by memory based and model based collaborative filtering – volume: 2 issue: 2 year: 2022 ident: 10.1016/j.jjimei.2025.100322_bib0039 article-title: How do context-aware artificial intelligence algorithms used in fitness recommender systems? A literature review and research agenda publication-title: International Journal of Information Management Data Insights doi: 10.1016/j.jjimei.2022.100139 – volume: 105 start-page: 657 year: 2024 ident: 10.1016/j.jjimei.2025.100322_bib48 article-title: A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media publication-title: J. Inst. Eng. India Ser. B doi: 10.1007/s40031-024-00999-z – start-page: 2493 year: 2020 ident: 10.1016/j.jjimei.2025.100322_bib0011 article-title: Deep multifaceted transformers for multi-objective ranking in large-scale e-commerce recommender systems – year: 2015 ident: 10.1016/j.jjimei.2025.100322_bib0025 article-title: Chapter 5 evaluating the quality of recommender systems – volume: 10 start-page: 100303 issue: 2 year: 2024 ident: 10.1016/j.jjimei.2025.100322_bib0006 article-title: A personalized product recommendation model in e-commerce based on retrieval strategy publication-title: Journal of Open Innovation: Technology, Market, and Complexity doi: 10.1016/j.joitmc.2024.100303 – year: 1997 ident: 10.1016/j.jjimei.2025.100322_bib0046 – volume: 188 year: 2020 ident: 10.1016/j.jjimei.2025.100322_bib0009 article-title: A new similarity measure for collaborative filtering-based Recommender Systems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.105058 – year: 2019 ident: 10.1016/j.jjimei.2025.100322_bib0008 |
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