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International Journal of Digital Multimedia Broadcasting
Volume 2019, Article ID 1326831, 10 pages
https://doi.org/10.1155/2019/1326831
Research Article

QoS Prediction for Neighbor Selection via Deep Transfer Collaborative Filtering in Video Streaming P2P Networks

1School of Computer and Control Engineering, Yantai University, Yantai 264005, China
2China National Nuclear Corporation, Beijing 100045, China

Correspondence should be addressed to Wenming Ma; moc.621@utymwm

Received 21 September 2018; Accepted 25 October 2018; Published 1 January 2019

Guest Editor: Yuanlong Cao

Copyright © 2019 Wenming Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To expand the server capacity and reduce the bandwidth, P2P technologies are widely used in video streaming systems in recent years. Each client in the P2P streaming network should select a group of neighbors by evaluating the QoS of the other nodes. Unfortunately, the size of video streaming P2P network is usually very large, and evaluating the QoS of all the other nodes is resource-consuming. An attractive way is that we can predict the QoS of a node by taking advantage of the past usage experiences of a small number of the other clients who have evaluated this node. Therefore, collaborative filtering (CF) methods could be used for QoS evaluation to select neighbors. However, we might use different QoS properties for different video streaming policies. If a new video steaming policy needs to evaluate a new QoS property, but the historical experiences include very few evaluation data for this QoS property, CF methods would incur severe overfitting issues, and the clients then might get unsatisfied recommendation results. In this paper, we proposed a novel neural collaborative filtering method based on transfer learning, which can evaluate the QoS with few historical data by evaluating the other different QoS properties with rich historical data. We conduct our experiments on a large real-world dataset, the QoS values of which are obtained from 339 clients evaluating on the other 5825 clients. The comprehensive experimental studies show that our approach offers higher prediction accuracy than the traditional collaborative filtering approaches.