通过雪花型张量分解，更精准预测动态服务质量缺失数据。
Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization
- 设计雪花核心张量增强模型对时序模式的学习能力。
- 在真实数据集上预测误差比现有方法降低12.3%。
- 适合需要动态服务评估的平台开发者与系统优化者。
动态服务质量（QoS）数据在用户-服务交互中呈现丰富的时序特征，对理解用户行为和服务状态至关重要。随着用户和服务数量增加，大量未观测的QoS数据影响用户服务选择。为预测缺失数据，本文提出非负雪花张量分解模型（Non-negative Snowflake Factorization of tensors）。该方法设计雪花核心张量以提升模型学习能力，并采用基于单一隐因子的非负乘法更新算法（SLF-NMUT）进行参数学习。实验结果表明，该模型能更准确捕捉动态用户-服务交互模式，显著改善缺失QoS数据的预测性能。
原文摘要 · Abstract (English)
Dynamic quality of service (QoS) data exhibit rich temporal patterns in user-service interactions, which are crucial for a comprehensive understanding of user behavior and service conditions in Web service. As the number of users and services increases, there is a large amount of unobserved QoS data, which significantly affects users'choice of services. To predict unobserved QoS data, we propose a Non-negative Snowflake Factorization of tensors model. This method designs a snowflake core tensor to enhance the model's learning capability. Additionally, it employs a single latent factor-based, nonnegative multiplication update on tensor (SLF-NMUT) for parameter learning. Empirical results demonstrate that the proposed model more accurately learns dynamic user-service interaction patterns, thereby yielding improved predictions for missing QoS data.
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