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Intent-Based Trust Evaluation
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AI
DOI：10.1109/TKDE.2025.3624874.png)
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Abstract
En 中文
Trust relationships play a crucial role in various domains, such as social spam detection, retweet behavior analytics, and recommendation systems. Trust is often implicit and difficult to observe directly in the real world, as it is driven by people’s underlying intentions and motivations. Therefore, when evaluating trust, it is critical to analyze not only user behavior data but also the intentions behind these behaviors that lead to trust. Existing trust evaluation methods often neglect the underlying reasons behind connections, such as shared hobbies or belonging to the same community. Therefore, these methods cannot differentiate the genuine intentions that lead to trust, resulting in an inaccurate evaluation of hidden trust relationships. To address this issue, we propose a novel Intent-based model for Trust Evaluation (INTRUST). This model can distinguish the intent behind high-order information in social communities using hypergraphs. Initially, we used hyperedges to represent high-order correlations between user-to-item and user-to-user interactions. Then, we construct <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> intent prototypes, which serve as foundational elements to build trust. Furthermore, we distinguish <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-independent intent subgraphs from these high-order correlations. To enhance the generalization and robustness of the model, we employ self-supervised learning and construct contrastive views at the node-level, hyperedge-level, and node-hyperedge-level. Extensive experiments on real-world datasets demonstrate that our model outperforms state-of-the-art approaches in terms of trust evaluation accuracy and efficiency.
Keywords:
Trust evaluation
hypergraph
intent distanglement
contrastive learning
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