Context-Aware Personalization Architectures for Intelligent Recommendations and Digital Service Optimization
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805041Keywords:
Context-Aware Personalization, Intelligent Recommendations, User Modeling, Contextual Computing, Adaptive Services, Machine Learning, Digital Service OptimizationAbstract
A personalization that is situation-dependent has become a central feature of smart-recommender systems and optimization of content, service and interactions in digital media whereby platforms can customize the content, service and interactions to the different needs of the user and the dynamic environment. Nonetheless, the older models of personalisation normally work opposing a pre-established pattern or signal of behaviour and therefore, cannot comprehend and perceive the temporal, contextual and cross domain purpose the user. The scenario that the paper suggests is a Context-Aware Personalization Framework (CAPF) that incorporates the user profiles, past behavioral history, present contextual signals, semantic preference model and adaptive recommendation systems into a unified framework. The framework involves context acquisition layer where the multimodal signals are gathered, context modeling layer where dynamically changing user-context representations are generated and the intelligent recommendation layer which is a combination of machine learning, deep learning and contextual ranking. An optimization layer that operates on a feedback loop evaluates user reactions, and update personalization policies, a privacy and governance layer makes sure that they handle the data responsibly. The suggested structure gives the power of changes in real time, explainable recommendations, service optimizations and personalization of services which evolves in real time within the online platform. The architecture provides a scalable foundation to applications like e-commerce, online health care, e-learning, smart services and personalized content delivery.
References
[1] J. Zhang et al., “TLSAN: Time-aware long- and short-term attention network for next-item recommendation,” Neurocomputing, 2021.
[2] N. Bheemisetty, “AI-powered recommendation systems: Best practices and real-world applications,” International Journal of Future Innovative Science and Technology (IJFIST), vol. 7, no. 6, p. 13926, 2024.
[3] X. Zhang et al., “Dynamic intent-aware iterative denoising network for session-based recommendation,” Information Processing & Management, 2022.
[4] A. Pujahari et al., “Modeling users’ preference changes in recommender systems via time-dependent Markov random fields,” Expert Systems with Applications, 2023.
[5] Z. Liu et al., “Dual-view preference learning for adaptive recommendation,” IEEE Transactions on Knowledge and Data Engineering, 2023.
[6] T. Liu et al., “Dynamic and static representation learning network for recommendation,” IEEE Transactions on Neural Networks and Learning Systems, 2024.
[7] Y. Xiao et al., “A joint learning recommendation model for E-commerce platforms integrating long-term and short-term interests,” IEEE Transactions on Services Computing, 2024.
[8] P. Liu et al., “Dynamic attention-based explainable recommendation with textual and visual fusion,” Information Processing & Management, 2020.
[9] X. Gu et al., “Sequence neural network for recommendation with multi-feature fusion,” Expert Systems with Applications, 2022.
[10] L. Liu et al., “An interactive food recommendation system using reinforcement learning,” Expert Systems with Applications, 2024.
[11] L. Chen et al., “Keywords-enhanced contrastive learning model for travel recommendation,” Information Processing & Management, 2024.
[12] M. Imran et al., “ReFRS: Resource-efficient federated recommender system for dynamic and diversified user preferences,” ACM Transactions on Information Systems, 2023.
