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Abstract 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.

Smart machine-machine (M2M) interactions, such as those enabled by the Internet of Things (IoT), have enabled people and machines to communicate and make decisions together. Furthermore, these systems have become increasingly important in the commercial and industrial sectors over the previous two decades. The Industrial Internet of Things (IIoT) is a smart system comprising engineering equipment which can connect to one another to improve manufacturing operations. This task would become more complicated if the amount of energy used by the IIoT ecosystems, as well as the amount of network traffic they generate, increased dramatically. Consequently, decision-making processes during communication are essential for autonomous interaction in critical IoT infrastructure. Smart factories employ communication technology to track and gather information in real-time to enhance the output, effectiveness, and predictability while lowering the overall cost of vital operations. In this context, Industry 4.0 not only limits to addresses the issues of integrating technologies, but it also focuses on data collection, dissemination, utilization, and organization and also improves the delivery of the solution or services quicker with more sustainability. This study intends to create an NF-based communication system for IIoT platforms to leverage those benefits. The proposed model includes smart decision-making procedures to deal with communication issues. Compared with the many methods already in use, the suggested mechanism’s functional viability in the automated system is found to be optimal. Outcomes from simulations reveal that the suggested method has improved the accuracy and communication reliability of the IIoT platforms in comparison with the previous methods. Aside from these, the suggested model keeps the throughput of the local automation unit at 96.03% and the throughput of the production hall at 95.58% on average while maintaining the lowest average PLR of about 26.48% across different data rates.