aspg-18-4267-1792025585 20261015005305 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2026 18 2 R. Dhayanidhi Research Scholar, Dept of CSE Vel Tech Rangarajan Dr. Sagunthala, R & D Institute of Science and Technology, Chennai, India Rajalakshmi N. R. 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mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Georgia",serif;} 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. 2026 401 419 4267 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180228 https://americaspg.com/journal/18/article/4267 https://americaspg.com/storage/51769992028.pdf