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[2601.11036] Self-Augmented Mixture-of-Experts for QoS Prediction
Summary
This paper presents a self-augmented mixture-of-experts model aimed at improving Quality of Service (QoS) prediction by leveraging iterative refinement of predictions.
Why It Matters
QoS prediction is critical for enhancing user experience in service computing and personalized recommendations. This study addresses the challenge of sparse user-service interactions, offering a novel approach that could lead to more accurate predictions and better service quality.
Key Takeaways
- Introduces a self-augmented strategy for QoS prediction.
- Utilizes a mixture-of-experts model for collaborative estimation.
- Demonstrates improved performance over existing baseline methods.
- Addresses the challenge of sparsity in user-service feedback.
- Highlights the importance of iterative refinement in model predictions.
Computer Science > Machine Learning arXiv:2601.11036 (cs) This paper has been withdrawn by Kecheng Cai [Submitted on 16 Jan 2026 (v1), last revised 21 Feb 2026 (this version, v3)] Title:Self-Augmented Mixture-of-Experts for QoS Prediction Authors:Kecheng Cai, Chao Peng, Chenyang Xu, Xia Chen, Yi Wang, Shuo Shi, Qiyuan Liang View a PDF of the paper titled Self-Augmented Mixture-of-Experts for QoS Prediction, by Kecheng Cai and 6 other authors No PDF available, click to view other formats Abstract: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 net...