In the realm of intelligent human–machine interaction systems, delivering adaptive and user-specific responses is essential for enhancing operational efficiency and user satisfaction. Traditional systems often lack the ability to dynamically adapt to changing user behaviors, resulting in suboptimal interaction outcomes. This research proposes a novel hyper-personalization framework that integrates meta-learning with deep ensemble-based user classification to enable intelligent and context-aware human–machine interactions. The framework begins with advanced data preprocessing, followed by user behavior segmentation through a hybrid hierarchical K-Means clustering algorithm, capturing granular interaction patterns. A Neural Collaborative Filtering (NCF)-based deep ensemble model is then employed to model complex user-system interactions and improve predictive performance. To achieve hyper-personalization, the system incorporates Model-Agnostic Meta-Learning (MAML), which enables rapid adaptation to new or evolving user behavior with minimal retraining. This adaptive capability supports scalable deployment across diverse machine-interaction scenarios. Experimental results demonstrate a performance accuracy of 98.6%, outperforming baseline models such as CNN, Bi-LSTM, and CNN-LSTM. The proposed framework highlights the potential for next-generation intelligent systems that require scalable, context-aware, and personalized interaction mechanisms.
Keywords
Meta-Learning, Hyper Personalization, Deep Ensemble, User Classification, Contextual Recommendation.
R. V. Karthik and S. Ganapathy, “A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce,” Applied Soft Computing, vol. 108, p. 107396, Sep. 2021, doi: 10.1016/j.asoc.2021.107396.
N. Ramshankar and P. M. Joe Prathap, “A novel recommendation system enabled by adaptive fuzzy aided sentiment classification for E-commerce sector using black hole-based grey wolf optimization,” Sādhanā, vol. 46, no. 3, Jun. 2021, doi: 10.1007/s12046-021-01631-2.
W. Xie, Q. Dong, and H. Gao, “A Probabilistic Recommendation Method Inspired by Latent Dirichlet Allocation Model,” Mathematical Problems in Engineering, vol. 2014, no. 1, Jan. 2014, doi: 10.1155/2014/979147.
Z. Jiang and S. Gao, “An intelligent recommendation approach for online advertising based on hybrid deep neural network and parallel computing,” Cluster Computing, vol. 23, no. 3, pp. 1987–2000, Jul. 2019, doi: 10.1007/s10586-019-02959-5.
Z. Li, D. Jin, and K. Yuan, “Attentional factorization machine with review-based user–item interaction for recommendation,” Scientific Reports, vol. 13, no. 1, Aug. 2023, doi: 10.1038/s41598-023-40633-4.
S. Akram, S. Hussain, I. K. Toure, S. Yang, and H. Jalal, “ChoseAmobile: A Web-based Recommendation System for Mobile Phone Products,” Journal of Internet Technology, vol. 21, no. 4, Art. no. 4, Jul. 2020.
D. Pramod and P. Bafna, “Conversational recommender systems techniques, tools, acceptance, and adoption: A state of the art review,” Expert Systems with Applications, vol. 203, p. 117539, Oct. 2022, doi: 10.1016/j.eswa.2022.117539.
A. E. Attaoui, A. Boukhalfa, S. Rhouas, and N. E. Hami, “Custom application programming interface data extractor applied to the Klarna e-commerce dataset,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 31, no. 3, p. 1624, Sep. 2023, doi: 10.11591/ijeecs.v31.i3.pp1624-1632.
A. L. Karn et al., “RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis,” Electronic Commerce Research, vol. 23, no. 1, pp. 279–314, Oct. 2022, doi: 10.1007/s10660-022-09630-z.
P. Zicari, G. Folino, M. Guarascio, and L. Pontieri, “Discovering accurate deep learning based predictive models for automatic customer support ticket classification,” in Proceedings of the 36th Annual ACM Symposium on Applied Computing, Virtual Event Republic of Korea: ACM, Mar. 2021, pp. 1098–1101. doi: 10.1145/3412841.3442109.
D. K. Sharma, S. Lohana, S. Arora, A. Dixit, M. Tiwari, and T. Tiwari, “E-Commerce product comparison portal for classification of customer data based on data mining,” Materials Today: Proceedings, vol. 51, pp. 166–171, 2022, doi: 10.1016/j.matpr.2021.05.068.
R. Logesh, V. Subramaniyaswamy, D. Malathi, N. Sivaramakrishnan, and V. Vijayakumar, “Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method,” Neural Comput & Applic, vol. 32, no. 7, pp. 2141–2164, Apr. 2020, doi: 10.1007/s00521-018-3891-5.
L. Wang, S. Mistry, A. A. Hasan, A. O. Hassan, Y. Islam, and F. A. Junior Osei, “Implementation of a Collaborative Recommendation System Based on Multi-Clustering,” Mathematics, vol. 11, no. 6, p. 1346, Mar. 2023, doi: 10.3390/math11061346.
F. E. Zaizi, S. Qassimi, and S. Rakrak, “Multi-objective optimization with recommender systems: A systematic review,” Information Systems, vol. 117, p. 102233, Jul. 2023, doi: 10.1016/j.is.2023.102233.
Z. Zhu, S. Wang, F. Wang, and Z. Tu, “Recommendation networks of homogeneous products on an E-commerce platform: Measurement and competition effects,” Expert Systems with Applications, vol. 201, p. 117128, Sep. 2022, doi: 10.1016/j.eswa.2022.117128.
X. Wang, L. Yu, S. Zhang, S. Wu, B. Yu, and X. Ye, “Referral strategy of e‐commerce platform under different selling modes,” Managerial and Decision Economics, vol. 44, no. 6, pp. 3451–3484, Jun. 2023, doi: 10.1002/mde.3890.
B. K. Shah, A. K. Jaiswal, A. Shroff, A. K. Dixit, O. N. Kushwaha, and N. K. Shah, “Sentiments Detection for Amazon Product Review,” 2021 International Conference on Computer Communication and Informatics (ICCCI), Jan. 2021, doi: 10.1109/iccci50826.2021.9402414.
L. Kolhe, A. K. Jetawat, and V. Khairnar, “Robust product recommendation system using modified grey wolf optimizer and quantum inspired possibilistic fuzzy C-means,” Cluster Computing, vol. 24, no. 2, pp. 953–968, Aug. 2020, doi: 10.1007/s10586-020-03171-6.
Z. Chen, L. Yang, R. M. Rodríguez, S. Xiong, K. Chin, and L. Martínez, “Power‐average‐operator‐based hybrid multiattribute online product recommendation model for consumer decision‐making,” International Journal of Intelligent Systems, vol. 36, no. 6, pp. 2572–2617, Mar. 2021, doi: 10.1002/int.22394.
S. Bandyopadhyay, S. S. Thakur, and J. K. Mandal, “Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society,” Innovations in Systems and Software Engineering, vol. 17, no. 1, pp. 45–52, Aug. 2020, doi: 10.1007/s11334-020-00372-5.
A. Suresh and M. J. Carmel Mary Belinda, “Online product recommendation system using gated recurrent unit with Broyden Fletcher Goldfarb Shanno algorithm,” Evolutionary Intelligence, vol. 15, no. 3, pp. 1861–1874, Mar. 2021, doi: 10.1007/s12065-021-00594-x.
P. K. Padigela and R. Suguna, “Segmentation of E-commerce users based on cart abandonment and product recommendation through collaborative filtering: the moderating effect of exorbitant pricing,” International Journal of System Assurance Engineering and Management, May 2021, doi: 10.1007/s13198-021-01122-x.
V. Boppana and P. Sandhya, “Web crawling based context aware recommender system using optimized deep recurrent neural network,” J Big Data, vol. 8, no. 1, p. 144, Dec. 2021, doi: 10.1186/s40537-021-00534-7.
“GitHub - paulsamuel-we/E-commerce-Customer-Behaviour-Dataset: This repository features a synthetic dataset for educational purposes, covering customer behavior and purchasing patterns on an e-commerce platform. It includes demographics, browsing and purchase history, and product reviews. Created by Gretel AI, it’s ideal for learning and research in data science and machine learning.,” GitHub. Accessed: Dec. 04, 2024. [Online]. Available: https://github.com/paulsamuel-we/E-commerce-Customer-Behaviour-Dataset.
Q. Li, X. Li, B. Lee, and J. Kim, “A Hybrid CNN-Based Review Helpfulness Filtering Model for Improving E-Commerce Recommendation Service,” Applied Sciences, vol. 11, no. 18, p. 8613, Sep. 2021, doi: 10.3390/app11188613.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Siva Rama Prasad Kollu and Yugandhar Garapati;
Methodology: Yugandhar Garapati;
Software: Siva Rama Prasad Kollu;
Data Curation: Siva Rama Prasad Kollu and Yugandhar Garapati;
Writing- Original Draft Preparation: Siva Rama Prasad Kollu;
Visualization: Yugandhar Garapati;
Investigation: Siva Rama Prasad Kollu and Yugandhar Garapati;
Supervision: Yugandhar Garapati;
Validation: Siva Rama Prasad Kollu;
Writing- Reviewing and Editing: Siva Rama Prasad Kollu and Yugandhar Garapati;
All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr.Yugandhar Garapati for this research completion and support.
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Yugandhar Garapati
Department of Computer Science and Engineering, GITAM Deemed to be University, Hyderabad, Telangana, India.
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Cite this article
Siva Rama Prasad Kollu and Yugandhar Garapati, “Meta Learning Driven Hyper Personalization Framework Using Deep Ensemble User Classification for Intelligent Human Machine Interaction Systems”, Journal of Machine and Computing, vol.5, no.4, pp. 2038-2055, October 2025, doi: 10.53759/7669/jmc202505159.
