TrustFormer：面向任务的跨时间与跨维度Transformer用于特定任务的多维信任评估
TrustFormer: Cross-Temporal and Cross- Dimensional Transformer for Task-Specific Multi-Dimensional Trust Evaluation
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中文总结 AI 辅助
针对动态协同系统多维信任评估的异步性与依赖难题，提出TrustFormer框架，通过同步异构数据与跨时间、跨维度注意力机制提升评估准确率40.8%，实现更优协作者选择。
中文摘要 AI 辅助
在动态协同系统中，选择可靠的协作者对确保任务有效执行至关重要。现有信任评估方法常依赖单维度或标量表示，无法如实捕捉协作者的真实可信度，因此推动了向多维信任建模的转变。然而，由于不同维度收集的信任相关数据存在异步性，且这些数据中嵌入了复杂的维度内和维度间依赖关系，多维信任评估仍具挑战性。为应对这些挑战，我们提出TrustFormer，这是一个面向特定任务的多维信任评估框架。具体而言，TrustFormer利用任务标识符和设备生成的时间戳，同步历史协作中异构的信任相关数据；进一步采用跨时间与跨维度注意力机制，联合建模时间动态性和维度间相关性，从而有效从历史性能数据中学习潜在协作者的多维信任演化。此外，根据任务的多维资源需求，评估潜在协作者的多维资源信任；最后，通过综合这些多维信任轮廓，该框架实现最优协作者选择。实验结果表明，TrustFormer相较于现有方法，信任评估准确率提升了40.8%，并能实现更可靠的协作者选择。
英文摘要
In dynamic collaborative systems, the selection of reliable collaborators is critical to ensuring effective task execution. Existing trust evaluation methods often rely on unidimensional or scalar representations, which fail to faithfully capture a collaborator's true trustworthiness, thereby motivating a shift toward multi-dimensional trust modeling. However, due to the asynchrony of collected trust-related data across different dimensions, as well as the complex intra- and inter-dimensional dependencies embedded within these data, multi-dimensional trust evaluation remains challenging. To address these challenges, we propose TrustFormer, a task-specific multi-dimensional trust evaluation framework. Specifically, TrustFormer leverages task identifiers and device-generated timestamps to synchronize heterogeneous trust-related data across historical collaborations. It further employs cross-temporal and cross-dimensional attention mechanisms to jointly model temporal dynamics and inter-dimensional correlations, thereby effectively learning the multi-dimensional trust evolution of potential collaborators from historical performance data. In addition, according to the multi-dimensional resource requirements of tasks, potential collaborators' multi-dimensional resource trust is evaluated. Finally, by synthesizing these multi-dimensional trust profiles, the framework enables the optimal collaborator selection. Experimental results demonstrate that TrustFormer outperforms existing methods by yielding a 40.8% improvement in trust evaluation accuracy and enabling more reliable collaborator selection.
发表机构
- Western University（西安大略大学）
机构由 AI 辅助整理，请以论文原文为准。
