Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 1 2026 01 01 Hybrid Deep Learning and Evolutionary Feature Selection for Real-Time Product Recommendations 75 100 5911 10.22060/eej.2025.24679.5746 EN Lazarus Nisha Evangelin 1Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kanyakumari, Tamil Nadu, India. 0009-0001-9226-4999 Ravichandran Devi Department of Artificial Intelligence and Data Science, R.M.K Engineering College, Kavaraipettai, Tamil Nadu 601206, India.601206, India Sundar Raj Bharathi Department of Electrical and Electronics Engineering, S.A Engineering College, Chennai, 600077, India. Vallirathi Iyyadurai Rohini college of Engineering and Technology, Anjukramam, Tamil Nadu, India. Shyamalagowri Murugesan Department of Electrical and Electronics Engineering, K.S.Rangasamy College of Technology, Tiruchengode, India. Jehan Chelliah Department of Computer Science and Engineering, Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai-62, India. Journal Article 2025 09 04 The swift expansion of e-commerce has driven the creation of Recommendation Systems (RS) that help users navigate vast catalogues and make informed purchase decisions. This work presents a novel recommendation system framework integrating adaptive techniques for enhanced accuracy and efficiency. The system utilizes Adaptive Evolutionary Feature Selection (AEFS), a novel feature selection algorithm combining genetic algorithms and reinforcement learning to select the most relevant features from user interaction data, product details, and contextual data. The pre-processing stage comprises text tokenization, normalization, and stop-word removal, followed by feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Factor Modelling. User profiling is performed using Graph-based Profiling and Behavioural Profiling, allowing for a holistic view of user inclinations and preferences. The Bidirectional Encoder Representations from Transformers for Recommendations (BERT4Rec) model, which uses transformer-based architectures, is used for generating recommendations by capturing complex sequential relationships in user behaviour. This hybrid approach combines Collaborative Filtering (CF) and Content-based Filtering (CBF) to deliver accurate and personalized recommendations. Real-time recommendations are provided using a distilled model, ensuring scalability and efficiency for large-scale e-commerce platforms. The system continuously adapts through a feedback loop based on user interactions, using reinforcement learning to improve performance. With an accuracy of 98%, BERT4Rec achieves improvements of up to 18.45% across key metrics. The proposed framework enhances recommendation accuracy, achieves a feature reduction rate of 70%, and ensures a robust user experience in modern e-commerce environments. https://eej.aut.ac.ir/article_5911_84a955d5ff75f508ec01007bc2b9b301.pdf