用跨时间跨维度注意力建模协作信任演化，提升多维可信度评估准确率40.8%。
TrustFormer: Cross-Temporal and Cross- Dimensional Transformer for Task-Specific Multi-Dimensional Trust Evaluation
- 引入任务标识与时间戳同步异构信任数据，实现跨维度对齐。
- 通过交叉时序与跨维度注意力机制，学习协作方多维信任演化规律。
- 针对任务资源需求评估多维资源可信度，适合动态协作系统使用。
在动态协作系统中，选择可靠合作者对确保任务有效执行至关重要。现有信任评估方法多依赖单维度或标量表示，难以真实反映合作者的可信程度，促使向多维信任建模转变。然而，由于不同维度的信任数据存在采集异步性，且内部及跨维度间存在复杂依赖关系，多维信任评估仍具挑战。为此，本文提出TrustFormer——一种任务特定的多维信任评估框架。该框架利用任务标识符和设备生成的时间戳，同步历史协作中的异构信任数据；进一步采用跨时序与跨维度注意力机制，联合建模时间动态与跨维度相关性，从而从历史表现数据中有效学习潜在合作者的多维信任演化。此外，根据任务的多维资源需求，评估合作者的多维资源可信度。最终，通过融合多维信任画像，实现最优合作者选择。实验表明，TrustFormer相比现有方法信任评估准确率提升40.8%，显著增强合作者选择可靠性。
原文摘要 · Abstract (English)
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.
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