Hybrid Deep Learning and Evolutionary Feature Selection for Real-Time Product Recommendations

Document Type : Research Article

Authors

1 1Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kanyakumari, Tamil Nadu, India.

2 Department of Artificial Intelligence and Data Science, R.M.K Engineering College, Kavaraipettai, Tamil Nadu 601206, India.601206, India

3 Department of Electrical and Electronics Engineering, S.A Engineering College, Chennai, 600077, India.

4 Rohini college of Engineering and Technology, Anjukramam, Tamil Nadu, India.

5 Department of Electrical and Electronics Engineering, K.S.Rangasamy College of Technology, Tiruchengode, India.

6 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.

Keywords

Main Subjects

References

  1. K. Sahoo, C. Pradhan, R. K. Barik, H. Dubey, “DeepReco: deep learning based health recommender system using collaborative filtering,” 2019 Computation, vol. 7, no. 2, pp. 25, doi: https://www.mdpi.com/2079-3197/7/2/25#.
  2. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, R. Kashef, “Recommendation systems: Algorithms, challenges, metrics, and business opportunities,” 2020 Applied sciences, vol. 10, no. 21, pp. 7748, doi: https://www.mdpi.com/2076-3417/10/21/7748#.
  3. Fu, H. Qu, Z. Yi, L. Lu, Y. Liu, “A novel deep learning-based collaborative filtering model for recommendation system,” 2018 IEEE Transactions on cybernetics, vol. 49, no. 3, pp. 1084–1096, doi: https://doi.org/10.1109/TCYB.2018.2795041.
  4. Smith, G. Linden, “Two decades of recommender systems at Amazon.com,” 2017 IEEE Internet Computing, vol. 21, no. 3, pp. 12–18, doi: https://doi.org/10.1109/MIC.2017.72.
  5. Beheshti, S. Yakhchi, S. Mousaeirad, S. M. Ghafari, S. R. Goluguri, M. A. Edrisi, “Towards cognitive recommender systems,” 2020 Algorithms, vol. 13, no. 8, pp. 176, doi: https://www.mdpi.com/1999-4893/13/8/176#.
  6. Hassan, M. Hamada, “A neural networks approach for improving the accuracy of multi-criteria recommender systems,” 2017 Applied Sciences, vol. 7, no. 9, pp. 868, doi: https://www.mdpi.com/2076-3417/7/9/868#.
  7. Wu, X. Luo, M. Shang, Y. He, G. Wang, M. Zhou, “A deep latent factor model for high-dimensional and sparse matrices in recommender systems,” 2019 IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 7, pp. 4285–4296, doi: https://doi.org/10.1109/TSMC.2019.2931393.
  8. Bobadilla, S. Alonso, A. Hernando “Deep learning architecture for collaborative filtering recommender systems,” 2020 Applied Sciences, vol. 10, no. 7, pp. 2441, doi: https://www.mdpi.com/2076-3417/10/7/2441#.
  9. K. Paradarami, N. D. Bastian, J. L. Wightman, “A hybrid recommender system using artificial neural networks,” 2017 Expert Systems with Applications, vol. 83, pp. 300–313, doi: https://doi.org/10.1016/j.eswa.2017.04.046.
  10. Geetha, M. Safa, C. Fancy, D. Saranya, “A hybrid approach using collaborative filtering and content-based filtering for recommender system,” 2018 In Journal of Physics: Conference Series, vol. 1000, pp. 012101, doi:10.1088/1742-6596/1000/1/012101.
  11. Banik, S. C. Satapathy, M. Agarwal, “Advanced weighted hybridized approach for recommendation system,” 2023 International Journal of Web Information Systems, vol. 19, no. 1, pp. 1–18, doi: https://doi.org/10.1108/IJWIS-01-2022-0006.
  12. Hazrati, M. Elahi, “Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines,” 2021 Expert Systems, vol. 38, no. 3, pp. e12645, doi: https://doi.org/10.1111/exsy.12645.
  13. Zhang, T. Luo, F. Zhang, Y. Wu, “A recommendation model based on deep neural network,” 2018 IEEE Access, vol. 6, pp. 9454–9463, doi: https://doi.org/10.1109/ACCESS.2018.2789866.
  14. A. Gunawan, D. Suhartono, “Music recommender system based on genre using convolutional recurrent neural networks,” 2019 Procedia Computer Science, vol. 157, pp. 99–109, doi: https://doi.org/10.1016/j.procs.2019.08.146.
  15. Song, J. A. Westerhuis, N. Aben, M. Michaut, L. F. A. Wessels, A. K. Smilde, “Principal component analysis of binary genomics data,” 2019 Briefings in bioinformatics, vol. 20, no. 1, pp. 317–329, doi: https://doi.org/10.1093/bib/bbx119.
  16. Nilashi, O. Ibrahim, K. Bagherifard, “A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques,” 2018 Expert Systems with Applications, vol. 92, pp. 507–520, doi: https://doi.org/10.1016/j.eswa.2017.09.058.
  17. Bobadilla, R. Bojorque, A. H. Esteban, R. Hurtado, “Recommender systems clustering using Bayesian non-negative matrix factorization,” 2017 IEEE Access, vol. 6, pp. 3549–3564, doi: https://doi.org/10.1109/ACCESS.2017.2788138.
  18. Li, K. Li, J. An, W. Zheng, K. Li, “An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs,” 2019 Information Sciences, vol. 496, pp. 464–484, doi: https://doi.org/10.1016/j.ins.2018.07.060.
  19. J. Ko, L. Maystre, M. Grossglauser, “Collaborative recurrent neural networks for dynamic recommender systems,” 2016 In Asian Conference on Machine Learning, pp. 366–381.
  20. Chizari, K. Tajfar, M. N. Moreno-García, “Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems,” 2023 Information, vol. 14, no. 2, pp. 131, doi: https://www.mdpi.com/2078-2489/14/2/131#
  21. Heuillet, F. Couthouis, N. Díaz-Rodríguez, “Explainability in deep reinforcement learning,” 2021 Knowledge-Based Systems, vol. 214, pp. 106685, doi: https://doi.org/10.1016/j.knosys.2020.106685.
  22. A. Mantey, C. Zhou, V. Mani, J. K. Arthur, E. Ibeke, “Maintaining privacy for a recommender system diagnosis using blockchain and deep learning,” 2022 Human-centric computing and information sciences, vol. 13, no. 47, doi: https://doi.org/10.22967/HCIS.2023.13.047.
  23. Zhang, P. Li, X. Han, Y. Yang, Y. Cui, “Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models,” 2024 in IEEE Access, vol. 12, pp. 190079-190091, doi: 10.1109/ACCESS.2024.3516697.
  24. Thorat, B. K. Patle, S. K. Kashyap, “Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming,” 2023 Smart Agricultural Technology, vol. 3, pp. 100114, doi: https://doi.org/10.1016/j.atech.2022.100114.
  25. Dhawan, K. Singh, M. Yadav, “Hybrid Deep Learning Recommendation System for Accurate Movie and Product Review Predictions,” 2025 Scalable Computing: Practice and Experience, vol. 26, no. 4, pp. 1902-1918, https://doi.org/10.12694/scpe.v26i4.4449,  
  26. Sami, W. E. Adrousy, S. Sarhan, S. Elmougy, “A deep learning based hybrid recommendation model for internet users,” 2024 Scientific Reports, vol. 14, no. 1, pp. 29390. https://doi.org/10.1038/s41598-024-79011-z
  27. Mehbodniya, M. V. Rao, L. G. David, K. G. Nigel, P. Vennam, “Online product sentiment analysis using random evolutionary whale optimization algorithm and deep belief network,” 2022 Pattern Recognition Letters, vol. 159, pp. 1-8, https://doi.org/10.1016/j.patrec.2022.04.024
  28. Angamuthu, P. Trojovský, “Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems,” 2023 PeerJ Computer Science, vol. 9, pp. e1497, https://doi.org/10.7717/peerj-cs.1497
  29. Hou, W. Gu, W. C. Dong, L. Dang, “A deep reinforcement learning real-time recommendation model based on long and short-term preference,” 2023 International Journal of Computational Intelligence Systems, vol. 16, no. 1, pp. 4, https://doi.org/10.1007/s44196-022-00179-1
  30. Liu, C. H. Chen, P. Zheng, G. Zhang, “An adaptive parallel feature learning and hybrid feature fusion-based deep learning approach for machining condition monitoring,” 2022 IEEE Transactions on Cybernetics, vol. 53, no. 12, pp. 7584-7595, https://doi.org/10.1109/TCYB.2022.3178116
  31. M. Shoja, N. Tabrizi, “Customer reviews analysis with deep neural networks for e-commerce recommender systems,” 2019 IEEE Access, vol. 7, pp. 119121–119130, doi: https://doi.org/10.1109/ACCESS.2019.2937518.
  32. Sadikin, A. Fauzan, “Evaluation of Machine Learning Approach for Sentiment Analysis using Yelp Dataset,” 2013 European Journal of Electrical Engineering and Computer Science, vol. 7, no. 6, pp. 58–64, doi: https://doi.org/10.24018/ejece.2023.7.6.583.
  33. González, F. Ortega, D. Pérez-López, S. Alonso, “Bias and unfairness of collaborative filtering-based recommender systems in MovieLens dataset,” 2022 IEEE Access, vol. 10, pp. 68429–68439, doi: https://doi.org/10.1109/ACCESS.2022.3186719.
  34. Mhammedi S, El Massari H, Gherabi N, Amnai M, “Enhancing Book Recommendations on GoodReads: A Data Mining Approach Based Random Forest Classification,” 2023 In The Proceedings of the International Conference on Smart City Applications, pp. 395–409, doi: https://doi.org/10.1007/978-3-031-54376-0_36.
  35. Hidasi, “Session-based Recommendations with Recurrent Neural Networks,” 2015 arXiv preprint arXiv, 1511.06939, doi: https://doi.org/10.48550/arXiv.1511.06939.
  36. C. Kang, J. McAuley, “Self-attentive sequential recommendation,” 2018 In 2018 IEEE international conference on data mining (ICDM), pp. 197–206, doi: https://doi.org/10.1109/ICDM.2018.00035.
AUT Journal of Electrical Engineering
Volume 58, Issue 1
2026
Pages 75-100

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