2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 401-419, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
R. Dhayanidhi 1 * , Rajalakshmi N. R. 2 *
Doi: https://doi.org/10.54216/JISIoT.180228
Ecommerce Platforms specifically in Retail domain be it a brick and morter store or an online shopping application has enormous user data from the behavioral, click stream, page visits, abandoned carts, user think time or dwell time. And from the retail stores where the data captured from Internet of Things (IoT) with respect to the shelve movements, visitor counts, IoT signals arising from RFID tags, beacons, smart sensors, proximity to specific products, kiosk interactions, self-checkout kiosk provide enormous data for hyper personalization. Traditional Singular Value Decomposition (SVD) algorithms suffer with the data sparsity and computational complexity when fed with such large data. Also the SVD relies on the historical patterns to find latent features which may not be very much helpful for the cold start personalization. Consumer behaviors and patterns are non-linear, for ex- ample time spent near a shelf in a Retail Store or the time spent on a categories page in online application and with the filters of the categories. SVD might capture these main trends but will miss subtle high frequency signals that drive the hyper personalization. To overcome this problem, the proposed research employs a significant latent core factor SVD. The proposed technique includes decomposing a large and sparse matrix that captures real-time interactions between users and products into matrices that permit the proposed model to forecast personalized product recommendations based on existing data. Large Language Models (LLM) were used to improve the process of feature extraction post the data imputation after the initial data preprocessing. The proposed research employs the Amazon product review dataset to evaluate the proposed significant latent core SVD. When compared to traditional SVD and state-of-the-art methods such as LightGCN and BERT4Rec, the proposed significant latent core factor SVD achieves lower error rates.
E-Commerce , Product Recommendation System , Machine Learning , Singular Value Decomposition , Large Language Model , Internet of Things , Smart Retail , Edge Computing
[1] C. Li, I. Ishak, H. Ibrahim, M. Zolkepli, F. Sidi, and C. J. I. A. Li, “Deep learning-based recommendation system: Systematic review and classification,” IEEE Access, 2023.
[2] D. Roy and M. Dutta, “A systematic review and research perspective on recommender systems,” Journal of Big Data, vol. 9, no. 1, p. 59, 2022.
[3] H. Zhou, F. Xiong, and H. Chen, “A comprehensive survey of recommender systems based on deep learning,” Applied Sciences, vol. 13, no. 20, p. 11378, 2023.
[4] Da’u and N. Salim, “Recommendation system based on deep learning methods: A systematic review and new directions,” Artificial Intelligence Review, vol. 53, no. 4, pp. 2709–2748, 2020.
[5] Torkashvand, S. M. Jameii, and A. Reza, “Deep learning-based collaborative filtering recommender systems: A comprehensive and systematic review,” Neural Computing and Applications, vol. 35, no. 35, pp. 24783–24827, 2023.
[6] Daza, N. D. G. Rueda, M. S. A. Sánchez, W. F. R. Espíritu, and M. E. C. Quiñones, “Sentiment analysis on e-commerce product reviews using machine learning and deep learning algorithms: A bibliometric analysis and systematic literature review, challenges and future works,” International Journal of Information Management Data Insights, vol. 4, no. 2, p. 100267, 2024.
[7] Suresh, M. Carmel, and M. Belinda, “A comprehensive study of hybrid recommendation systems for e-commerce applications,” International Journal of Advanced Science and Technology, vol. 29, no. 3, pp. 4089–4101, 2020.
[8] L. Liu, “E-commerce personalized recommendation based on machine learning technology,” Mobile Information Systems, vol. 2022, no. 1, p. 1761579, 2022.
[9] K. Wu and K. Chi, “Enhanced e-commerce customer engagement: A comprehensive three-tiered recommendation system,” Journal of Knowledge Learning Science Technology, vol. 2, no. 3, pp. 348–359, 2023.
[10] N. Chabane et al., “Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms,” PLOS ONE, vol. 17, no. 12, p. e0278364, 2022.
[11] Hasan, Z. B. Yusof, and M. Karim, “Machine learning algorithms for personalized product recommendations and enhanced customer experience in e-commerce platforms,” International Journal of Applied Machine Learning, vol. 4, no. 11, pp. 1–15, 2024.
[12] S. Sharma, V. Rana, and V. Kumar, “Deep learning-based semantic personalized recommendation system,” International Journal of Information Management Data Insights, vol. 1, no. 2, p. 100028, 2021.
[13] H. Bastani, P. Harsha, G. Perakis, D. Singhvi, and S. O. Management, “Learning personalized product recommendations with customer disengagement,” Manufacturing & Service Operations Management, vol. 24, no. 4, pp. 2010–2028, 2022.
[14] Iftikhar, M. A. Ghazanfar, M. Ayub, Z. Mehmood, and M. Maqsood, “An improved product recommendation method for collaborative filtering,” IEEE Access, vol. 8, pp. 123841–123857, 2020.
[15] N. Padhy, S. Suman, T. S. Priyadarshini, and S. Mallick, “A recommendation system for e-commerce products using collaborative filtering approaches,” Engineering Proceedings, vol. 67, no. 1, p. 50, 2024.
[16] Islek and S. G. Oguducu, “A hierarchical recommendation system for e-commerce using online user reviews,” Electronic Commerce Research and Applications, vol. 52, p. 101131, 2022.
[17] Q. Li, X. Li, B. Lee, and J. Kim, “A hybrid CNN-based review helpfulness filtering model for improving e-commerce recommendation service,” Applied Sciences, vol. 11, no. 18, p. 8613, 2021.
[18] F. Colace, D. Conte, M. De Santo, M. Lombardi, D. Santaniello, and C. Valentino, “A content-based recommendation approach based on singular value decomposition,” Connection Science, vol. 34, no. 1, pp. 2158–2176, 2022.
[19] W.-E. Kong, T.-E. Tai, P. Naveen, and H. A. Santoso, “Performance evaluation on e-commerce recommender system based on KNN, SVD, co-clustering and ensemble approaches,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 63–76, 2024.
[20] Hssina, A. Grota, and M. Erritali, “Recommendation system using the k-nearest neighbors and singular value decomposition algorithms,” International Journal of Electrical and Computer Engineering, vol. 11, no. 6, pp. 5541–5548, 2021.
[21] H. Du, “Teaching research on e-commerce micro-media recommendation data analysis by integrating singular value decomposition algorithm,” Journal of Electrical Systems, vol. 20, no. 9s, pp. 204–211, 2024.
[22] Tripathi, R. Jain, and K. Tahiliani, “A recommender system based on variants of singular value decomposition,” in Data Analytics for Intelligent Systems: Techniques and Solutions. Bristol, UK: IOP Publishing, 2024, pp. 11-1–11-15.
[23] S. Rahman, “Extended collaborative filtering recommendation system with adaptive KNN and SVD,” International Journal of Engineering Management Research, vol. 13, no. 4, 2023.
[24] S. Sachin, A. Tripathi, N. Mahajan, S. Aggarwal, and P. Nagrath, “Sentiment analysis using gated recurrent neural networks,” SN Computer Science, vol. 1, pp. 1–13, 2020.
[25] Y. M. Latha and B. S. Rao, “Amazon product recommendation system based on a modified convolutional neural network,” ETRI Journal, vol. 46, no. 4, pp. 633–647, 2024.
