Document Type : Research Article
Authors
1
Department of Computer science and Engineering, Noorul Islam Centre for Higher Education, Kanyakumari, Tamil Nadu, India
2
Assistant Professor, Department of Artificial Intelligence and Data Science, R.M.K Engineering College, Kavaraipettai, Tamil Nadu 601206, India
3
Associate Professor, Department of Electrical and Electronics Engineering, S.A Engineering College, Chennai, 600077, India
4
Associate Professor, Rohini college of Engineering and Technology, Anjukramam, Tamil Nadu, India
5
Associate Professor, Department of Electrical and Electronics Engineering, K.S.Rangasamy College of Technology, Tiruchengode, India
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Associate Professor, Department of Computer science and Engineering, Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai-62, India
Abstract
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.
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