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Realization and Prediction of IoT-based Dynamic Social Interactions for the Future Recommendations
Kaladevi A C, Vinoth Kumar V, A K Velmurugan, K Gunasekaran, B. Swapna and V. Dhilip Kumar

In IoT-based social networking, devices such as smartphones, wearable devices, and smart home appliances can be used to collect data about users’ activities, preferences, and social connections. Analyzing such information may lead to more tailored suggestions, improved social interactions, and an overall better user experience. To realize and predict IoT-based dynamic social interactions for future recommendations, machine-learning algorithms are used to evaluate data gathered from IoT devices and social networking apps to provide user-specific suggestions. To do this, the system would have to collect and analyze information from a wide variety of sources, such as social media activity via IoT gadgets, location data, device usage statistics, and usage patterns. The most significant difficulty is maintaining compatibility with ever-evolving user preferences and routines. Recommendations tailored to the user’s preferences and actions can be generated using machine learning techniques. So, in this research, we combine modular neural networks with the reinforcement learning approach to create robust and versatile learning systems that can easily adjust to new contexts and challenges. The use of modular neural networks allows for the decomposition of large tasks into smaller, more feasible sub-tasks, all of which is assigned to a different module. The modules are trained to carry out their functions and coordinate their activities using reinforcement-learning techniques. When applied to IoT-based social networking applications, the combination of modular neural networks and reinforcement learning algorithms can produce more efficient and flexible learning systems that can continuously learn from user reviews/feedback and accommodate those recommendations appropriately. Finally, the suggested model is experimentally evaluated using an appropriate testbed, and the findings are compared and assessed in light of some recently existing models. The results show that the suggested paradigm effectively addresses IoT-based social networking activities, which bodes well for future suggestions. Moreover, it is observed that the predicted accuracy is higher than 93% in all the relevant contexts.

Keywords: Machine learning, prediction, recommendation, IoT, ERR, user satisfaction reward, module, feedback