آمار

تعداد نشریات9
تعداد شماره‌ها452
تعداد مقالات5,748
تعداد مشاهده مقاله8,234,484
تعداد دریافت فایل اصل مقاله6,752,160
Nisha Evangelin, Lazarus, Devi, Ravichandran, Bharathi, Sundar, Iyyadurai, Vallirathi, Murugesan, Shyamalagowri, Chelliah, Jehan. (1404). Hybrid Deep Learning and Evolutionary Feature Selection for Real-Time Product Recommendations. نشریه مهندسی عمران امیرکبیر, 58(1), 75-100. doi: 10.22060/eej.2025.24679.5746
Lazarus Nisha Evangelin; Ravichandran Devi; Sundar Raj Bharathi; Vallirathi Iyyadurai; Shyamalagowri Murugesan; Jehan Chelliah. "Hybrid Deep Learning and Evolutionary Feature Selection for Real-Time Product Recommendations". نشریه مهندسی عمران امیرکبیر, 58, 1, 1404, 75-100. doi: 10.22060/eej.2025.24679.5746
Nisha Evangelin, Lazarus, Devi, Ravichandran, Bharathi, Sundar, Iyyadurai, Vallirathi, Murugesan, Shyamalagowri, Chelliah, Jehan. (1404). 'Hybrid Deep Learning and Evolutionary Feature Selection for Real-Time Product Recommendations', نشریه مهندسی عمران امیرکبیر, 58(1), pp. 75-100. doi: 10.22060/eej.2025.24679.5746
Nisha Evangelin, Lazarus, Devi, Ravichandran, Bharathi, Sundar, Iyyadurai, Vallirathi, Murugesan, Shyamalagowri, Chelliah, Jehan. Hybrid Deep Learning and Evolutionary Feature Selection for Real-Time Product Recommendations. نشریه مهندسی عمران امیرکبیر, 1404; 58(1): 75-100. doi: 10.22060/eej.2025.24679.5746

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

AUT Journal of Electrical Engineering
مقاله 5، دوره 58، شماره 1، 2026، صفحه 75-100    اصل مقاله (2.2 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2025.24679.5746
نویسندگان
Lazarus Nisha Evangelin* 1؛ Ravichandran Devi2؛ Sundar Raj Bharathi3؛ Vallirathi Iyyadurai4؛ Shyamalagowri Murugesan5؛ Jehan Chelliah6
11Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kanyakumari, Tamil Nadu, India.
2Department of Artificial Intelligence and Data Science, R.M.K Engineering College, Kavaraipettai, Tamil Nadu 601206, India.601206, India
3Department of Electrical and Electronics Engineering, S.A Engineering College, Chennai, 600077, India.
4Rohini college of Engineering and Technology, Anjukramam, Tamil Nadu, India.
5Department of Electrical and Electronics Engineering, K.S.Rangasamy College of Technology, Tiruchengode, India.
6Department of Computer Science and Engineering, Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai-62, India.
چکیده
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.
کلیدواژه‌ها
AEFS algorithm؛ TF-IDF Vectorizer؛ BERT4Rec model؛ Recommendation System؛ Collaborative Filtering؛ Content based Filtering؛ Latent Factor Modelling
مراجع
  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.
آمار
تعداد مشاهده مقاله: 356
تعداد دریافت فایل اصل مقاله: 234


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب