Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendations
Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendations
复制标题
用于模糊感知社交推荐的深度学习嵌入式社交物联网
DOI：
10.1109/tnse.2021.3049262
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发表时间：
2022-05-01
影响因子：
6.6
通讯作者：
Lin, Jerry Chun-Wei
中科院分区：
文献类型：
--
作者：
Guo, Zhiwei;Yu, Keping;Lin, Jerry Chun-Wei
随着用户对个性化社会服务需求的不断增加，社会推荐已成为学术界关注的重要问题。然而，目前关于SR的研究普遍面临两个主要挑战。一方面，SR缺乏强大的在线数据管理能力。另一方面，SR没有考虑到偏好反馈的模糊性。为了弥补这些差距，提出了一种用于模糊感知SR （SIoT-SR）的深度学习嵌入式社交物联网（IoT）。针对社交计算场景，开发了一种社交物联网架构，保证数据管理的可靠性。提出了一种基于深度学习的图神经网络模型，该模型可以嵌入到模型中，作为实现模糊感知sr的核心算法。该设计不仅提供了适当的在线数据感知和管理，而且克服了sr中的偏好模糊问题。为了评估所提出的SIoT-SR的性能，选择了两个真实数据集建立实验场景。该方法使用三种不同的指标进行评估，选择五种典型方法作为基准。实验结果表明，所提出的SIoT-SR算法的性能比基准方法提高了至少10%，并且具有良好的鲁棒性。
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.