Buchi Reddy Ramakantha Reddy, Ramasamy Lokesh Kumar*
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4081-4107, 2024, DOI:10.32604/cmc.2024.057071
- 19 December 2024
Abstract Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products, leading to suboptimal user experiences. To address this, our study presents a Personalized Adaptive Multi-Product Recommendation System (PAMR) leveraging transfer learning and Bi-GRU (Bidirectional Gated Recurrent Units). Using a large dataset of user reviews from Amazon and Flipkart, we employ transfer learning with pre-trained models (AlexNet, GoogleNet, ResNet-50) to extract high-level attributes from product data, ensuring effective feature representation even with limited data. Bi-GRU captures both spatial and sequential dependencies in user-item interactions. The innovation of this study lies… More >
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