QoS Prediction for Mobile Edge Service Recommendation With Auto-Encoder

• Main Authors: Yuyu Yin, Weipeng Zhang, Yueshen Xu, He Zhang, Zhida Mai, Lifeng Yu
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: QoS prediction; service recommendation; auto-encoder; features learning; similarity computation
• Online Access: https://ieeexplore.ieee.org/document/8706984/

In the mobile edge computing environment, there are a large number of mobile edge services which are the carriers of various mobile intelligent applications. So how to recommend the most suitable candidate from such a huge number of available services is an urgent task, especially the recommendation task based on quality-of-service (QoS). In traditional service recommendation, collaborative filtering (CF) has been studied in academia and industry. However, due to the mobility of users and services, there exist several defects that limit the application of the CF-based methods, especially in an edge computing environment. The most important problem is the cold-start. In this paper, we propose an ensemble model which combines the model-based CF and neighborhood-based CF. Our approach has two phases, i.e., global features learning and local features learning. In the first phase, to alleviate the cold-start problem, we propose an improved auto-encoder which deals with sparse inputs by pre-computing an estimate of the missing QoS values and can obtain the effective hidden features by capturing the complex structure of the QoS records. In the second phase, to further improve prediction accuracy, a novel computation method is proposed based on Euclidean distance that aims to address the overestimation problem. We introduce two new concepts, common invocation factor and invocation frequency factor, in similarity computation. Then we propose three prediction models, containing two individual models and one hybrid model. The two individual models are proposed to utilize user similar neighbors and service similar neighbors, and the hybrid model is to utilize all neighbors. The experiments conducted in a real-world dataset show that our models can produce superior prediction results and are not sensitive to parameter settings.