Smart Internet of Everything Model for Knowledge-Graph-Based Reliable Recommendation

作者：Nasrullah Khan, Ghulam Muhammad, Xiao‐Yuan Jing · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3599082 · 被引用次数：2 · 研究领域：Technology and Data Analysis

User attention, doubt and anxiety about reliability in intelligent decisions is continuously increasing with increase in the data overload on the Internet of Things (IoT) frameworks. Recommender system (RS) faces noisy inputs and provides vague recommendations with target disparity, explanation ambiguity, and performance biasness as a consequence. The inactive or less interactive users suffer more from these issues due to the lack of sufficient information about their browsing history on the system’s end. In this work, therefore, we introduce smart Internet of Everything model for knowledge graph-based reliable recommendation (KGR) to overcome irrelevant feeds-in from the IoT networks and ensure pertinence-based data quality at the knowledge base to address the highlighted research challenges in the current IoT-based RSs. Particularly, we verify relevance of the incoming contents with the concerned application scenarios, translate the received data to the embedding space, and apply data quality inspection check on the underlying data. We independently encapsulate user-to-item interactions, and provide independent streams of low-level representations of users and items to the prediction module. It uses deep nonnegative matrix factorization technique to process user-item representations and acquire the required preferences. In experiments on four real world datasets, KGR outperforms the-state-of-the-art methods by successfully meeting the aforementioned challenges.