Self-Augmented Mixture-of-Experts for QoS Prediction

TL;DR

This paper introduces a self-augmented mixture-of-experts model for QoS prediction that iteratively refines estimates by leveraging its own predictions, effectively addressing data sparsity and improving accuracy.

Contribution

The paper proposes a novel self-augmented mixture-of-experts framework that enhances QoS prediction through iterative refinement and expert collaboration.

Findings

Outperforms existing baseline methods on benchmark datasets.

Achieves competitive accuracy in QoS prediction tasks.

Effectively mitigates data sparsity issues in user-service interactions.

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 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.