The researchers in the current era provided many new recommendation methodologies. Though various recommendation techniques exist, there is a need to develop a unique technique for capturing latent factors and patterns from sparse and high-dimensional data in pervasive environments, specifically for optimizing dynamic recommendations. This study proposes a hybrid approach for optimizing dynamic recommendations in pervasive environments by combining Non-Negative Matrix Factorization (NMF) with deep learning and reinforcement learning techniques. The goal is to overcome the challenge of capturing latent factors and patterns from sparse and high-dimensional data. By leveraging NMF, meaningful latent factors are extracted, while deep learning, specifically Faster recurrent neural networks (FRNNs), learns complex feature representations. Reinforcement learning algorithms optimize the recommendation policy based on user feedback. This Hybrid Context-Aware Optimized Recommendation (HCOR) approach improves recommendation accuracy and relevance in pervasive environments, adapts to changing contexts, and enhances user experiences. The performance benefits are achieved by effectively capturing latent factors and patterns, resulting in improved accuracy and the ability to provide personalized and context-aware recommendations. The performance indicators to validate the research work include the recommendations' accuracy, relevance, and adaptability in pervasive environments. Additionally, metrics, such as precision, recall, and F1-score, are used to evaluate the effectiveness of the hybrid approach in capturing latent factors and patterns. User feedback and satisfaction are also measured to assess the impact on user experiences. The HCOR approach shows substantial performance gains, measuring a precision of 0.932, a recall of 0.922, and an F1-score of 0.943, which indicates the excellent ability of the approach to deliver accurate and personalized recommendations in a pervasive environment.

A. C. Kaladevi

V. Vinoth Kumar

T. R. Mahesh

Suresh Guluwadi

International Journal of Computational Intelligence Systems

《国际计算智能系统杂志》发表了关于应用计算智能各个方面的原创研究，特别是针对证明使用源自计算智能理论的技术和方法的论文。计算智能的核心理论是模糊逻辑、神经网络、进化计算和概率推理。该期刊仅发表与计算智能使用相关的文章，广泛涵盖以下主题：-自主推理-生物信息学-云计算-状态监测-数据科学-数据挖掘-数据可视化-决策支持系统-故障诊断-智能信息检索-人机交互和界面-图像处理-互联网和网络-噪声分析-模式识别-预测系统-电力（核）安全系统-过程和系统控制-实时系统-风险分析和安全相关问题-机器人-信号和图像处理-物联网和智能环境-系统集成-系统控制-系统建模和优化-电信-时间序列预测-预警系统-虚拟现实-WEB智能-深度学习

The International Journal of Computational Intelligence Systems publishes original research on various aspects of computational intelligence applications, particularly papers that demonstrate the use of techniques and methods derived from computational intelligence theories. The core theories of computational intelligence include fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning. This journal exclusively publishes articles related to the use of computational intelligence, covering a wide range of topics such as autonomous reasoning, bioinformatics, cloud computing, state monitoring, data science, data mining, data visualization, decision support systems, fault diagnosis, intelligent information retrieval, human-computer interaction and interfaces, image processing, internet and networks, noise analysis, pattern recognition, predictive systems, power (nuclear) safety systems, process and system control, real-time systems, risk analysis and safety-related issues, robotics, signal and image processing, the Internet of Things and smart environments, system integration, system control, system modeling and optimization, telecommunications, time series forecasting, early warning systems, virtual reality, web intelligence, and deep learning.

《国际计算智能系统杂志》发表关于计算智能应用各个方面的原创研究，特别是证明使用源自计算智能理论的技术和方法的论文。计算智能的核心理论包括模糊逻辑、神经网络、进化计算和概率推理。该期刊仅发表与计算智能相关的文章，广泛涵盖以下主题：自主推理、生物信息学、云计算、状态监测、数据科学、数据挖掘、数据可视化、决策支持系统、故障诊断、智能信息检索、人机交互与界面、图像处理、互联网与网络、噪声分析、模式识别、预测系统、电力（核）安全系统、过程与系统控制、实时系统、风险分析与安全相关问题、机器人、信号与图像处理、物联网与智能环境、系统集成、系统控制、系统建模与优化、电信、时间序列预测、预警系统、虚拟现实、WEB智能及深度学习。

Category	Quartile
计算机科学	4
计算机科学, 计算机人工智能	4
计算机科学, 计算机跨学科应用	4

Optimizing Personalized and Context-Aware Recommendations in Pervasive Computing Environments

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS	3