A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

TL;DR

This paper introduces PLRes, a novel ResNet-based method that incorporates probability distribution and location features to improve QoS prediction accuracy, especially in sparse data scenarios.

Contribution

The paper proposes a new deep learning model that leverages distribution and location information, addressing gradient issues and outperforming existing methods in QoS prediction.

Findings

PLRes outperforms LDCF by 12.35%-15.37% in MAE on sparse datasets.

Incorporating distribution and location features improves QoS prediction accuracy.

ResNet alleviates gradient disappearance issues in deep models.

Abstract

In recent years, the number of online services has grown rapidly, invoke the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering(CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity, and the problem of network deepening gradient disappearance is serious, so the effect of QoS prediction is unsatisfactory. To address these problems, we propose an advanced probability distribution and location-aware ResNet approach for QoS Prediction(PLRes). This approach considers the historical invocations probability distribution and location characteristics of users and services, and first use the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation. A series of experiments are conducted on a real-world web service dataset WS-DREAM. The results indicate that PLRes model is effective for QoS prediction and at the density of 5%-30%, which means the data is sparse, it significantly outperforms a state-of-the-art approach LDCF by 12.35%-15.37% in terms of MAE.