用模型自生成数据迭代优化，提升服务质量预测精度。
Self-Augmented Mixture-of-Experts for QoS Prediction
- 让模型自己生成部分预测值，再回传给自身反复优化。
- 在多个基准数据集上优于现有方法，显著缓解数据稀疏问题。
- 适合做个性化推荐和系统服务质量评估的研究者参考。
服务质量（QoS）预测是服务计算与个性化推荐中的核心问题。给定用户与服务的特征及部分交互反馈（如响应时间、可用性等），目标是预测未观察到的用户-服务对的QoS值。该任务面临交互数据极度稀疏的挑战。为此，本文提出一种自增强策略：将模型自身的预测结果部分掩码后回传，用于下一轮预测。在此基础上，设计了自增强混合专家模型（Self-Augmented MoE），多个专家网络通过迭代协作共同估计QoS值。实验表明，该方法在多个基准数据集上优于现有基线，性能表现优异。
原文摘要 · Abstract (English)
Quality of Service (QoS) prediction is one of the most fundamental problems in service computing and personalized recommendation. In the problem, there is a set of users and services, each associated with a set of descriptive features. Interactions between users and services produce feedback values, typically represented as numerical QoS metrics such as response time or availability. Given the observed feedback for a subset of user-service pairs, the goal is to predict the QoS values for the remaining pairs. A key challenge in QoS prediction is the inherent sparsity of user-service interactions, as only a small subset of feedback values is typically observed. To address this, we propose a self-augmented strategy that leverages a model's own predictions for iterative refinement. In particular, we partially mask the predicted values and feed them back into the model to predict again. Building on this idea, we design a self-augmented mixture-of-experts model, where multiple expert networks iteratively and collaboratively estimate QoS values. We find that the iterative augmentation process naturally aligns with the MoE architecture by enabling inter-expert communication: in the second round, each expert receives the first-round predictions and refines its output accordingly. Experiments on benchmark datasets show that our method outperforms existing baselines and achieves competitive results.
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