Shunmei Meng , Xuyun Zhang , Nan Liu , Longchuan Tu , Qianmu Li
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引用次数: 0
Abstract
Explainable Recommendation has attracted increasing attention due to the growing significance of data privacy and model security in recommendation systems. However, the effectiveness of robust and security-sensitive recommendation methods may be constrained by limited observed data, potentially resulting in suboptimal accuracy and reliability. Although introducing multi-source side information helps mitigate data sparsity issues and improve recommendation performance, it also presents new challenges, including semantic disparities and noise interference. In view of these observations, we propose a Knowledge&Social-based collaborative method with Contrastive Graph Structure Learning for explainable recommendation, named KSCGSL. It establishes multi-view representations for users and items with explainable learning based on knowledge-enhanced semantic-aware modeling and social network-driven preference learning, both refined via contrastive graph structure optimization. Specifically, KSCGSL introduces a dual graph augmentation mechanism based on knowledge graph and semantic awareness for item embedding learning. For user modeling, it captures user preferences from user-item interaction analysis and augments them through social relations. To solve the inherent semantic inconsistencies across multiple views and mitigate noise interference, contrastive graph structural learning is incorporated to optimize embedding learning and filter structural noise. Experiments conducted on three publicly available datasets demonstrate that KSCGSL achieves significant improvements in recommendation accuracy with explainable manners.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.