Product collaborative filtering based recommendation systems 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 power...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096825000047 |
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| _version_ | 1832576385446576128 |
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| author | Trang Trinh Van-Ho Nguyen Nghia Nguyen Duy-Nghia Nguyen |
| author_facet | Trang Trinh Van-Ho Nguyen Nghia Nguyen Duy-Nghia Nguyen |
| author_sort | Trang Trinh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0755893e659e4224888d98140f991da8 |
| institution | Kabale University |
| issn | 2667-0968 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj-art-0755893e659e4224888d98140f991da82025-01-31T05:12:38ZengElsevierInternational Journal of Information Management Data Insights2667-09682025-06-0151100322Product collaborative filtering based recommendation systems for large-scale E-commerceTrang Trinh0Van-Ho Nguyen1Nghia Nguyen2Duy-Nghia Nguyen3University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, VietnamUniversity of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam; Corresponding author.University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, VietnamUniversity of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, VietnamThe 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.http://www.sciencedirect.com/science/article/pii/S2667096825000047Apache sparkCollaborative filteringE-commerceLarge-scaleParallel and distributed computingRecommendation systems |
| spellingShingle | Trang Trinh Van-Ho Nguyen Nghia Nguyen Duy-Nghia Nguyen Product collaborative filtering based recommendation systems for large-scale E-commerce International Journal of Information Management Data Insights Apache spark Collaborative filtering E-commerce Large-scale Parallel and distributed computing Recommendation systems |
| title | Product collaborative filtering based recommendation systems for large-scale E-commerce |
| title_full | Product collaborative filtering based recommendation systems for large-scale E-commerce |
| title_fullStr | Product collaborative filtering based recommendation systems for large-scale E-commerce |
| title_full_unstemmed | Product collaborative filtering based recommendation systems for large-scale E-commerce |
| title_short | Product collaborative filtering based recommendation systems for large-scale E-commerce |
| title_sort | product collaborative filtering based recommendation systems for large scale e commerce |
| topic | Apache spark Collaborative filtering E-commerce Large-scale Parallel and distributed computing Recommendation systems |
| url | http://www.sciencedirect.com/science/article/pii/S2667096825000047 |
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