Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendations

Abstract : With the increasing demand of users for personalized social services, social recommendation (SR) has been an important concern in academia. However, current research on SR universally faces two main challenges. On the one hand, SR lacks the considerable ability of robust online data management. On the other hand, SR fails to take the ambiguity of preference feedback into consideration. To bridge these gaps, a deep learning-embedded social Internet of Things (IoT) is proposed for ambiguity-aware SR (SIoT-SR). Specifically, a social IoT architecture is developed for social computing scenarios to guarantee reliable data management. A deep learning-based graph neural network model that can be embedded into the model is proposed as the core algorithm to perform ambiguity-aware SR. This design not only provides proper online data sensing and management but also overcomes the preference ambiguity problem in SR. To evaluate the performance of the proposed SIoT-SR, two real-world datasets are selected to establish experimental scenarios. The method is assessed using three different metrics, selecting five typical methods as benchmarks. The experimental results show that the proposed SIoT-SR performs better than the benchmark methods by at least 10% and has good robustness.
 ? In order to capture group-level preference features from individuals, existing methods were mostly established via aggregation and face two aspects of challenges: secure data management workflow is absent, and implicit preference feedbacks is ignored. ? For another, existing GRSs were developed by investigating how to effectively aggregate members into a group. ? It is recognized that existing researches concerning GRSs still face two aspects of challenges. One is absence of real-time online management, and the other is ignorance of situations of implicit feedbacks. ? Existing technical approaches mostly utilized explicit feedbacks to develop GRSs.
 ? This category of techniques allows decisions to be made based on probabilities attached to the facts related to the problem. It can be used to combine sensor data from two different sources. ? Therefore, the best method to tackle the problem of context awareness it to combine multiple models in such a way that, as a whole, they reduce weaknesses by complementing each other. ? In summary, problems in sensor technology and problems in reasoning techniques contribute to context conflicts. ? how can we apply those techniques to solve problems in the future in different paradigms such as the IoT, and to highlight open challenges and to discuss future research directions.
 • SAIoT-GR, composed of two main modules, is proposed to solve the above two aspects of challenges, respectively. It well fuses a secure IoT framework and AI algorithms. • Enough experiments on real-world datasets are carried out to prove efficiency of the proposed SAIoT-GR. • Among, the processing layer directly implements group recommendations by carrying the software module which is exactly the developed AI algorithm CBN model. All parts of SAIoT-GR work jointly to constitute the newly proposed GRS. • As some unsupervised methods are susceptible to initial value settings, an additional group of experiments are conducted to testify stability for the proposed SAIoT-GR.
 ? Reasoning performance can be measured using efficiency, soundness, completeness, and interoperability. Reasoning is also called inferencing. ? In order to address this inefficiency, significant amounts of middleware solutions are introduced by researchers. ? It is all about efficiency and effectiveness. For example, perform processing in the first few layers could reduce data communication. ? In an ideal context-aware framework for the IoT, multiple different context representation models should be incorporated together to improve their efficiency and effectiveness. ? In contrast, semantic ontologies may not perform well in terms of efficiency and query processing with large volumes of data.
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