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Mobile Information Systems
Volume 2017, Article ID 7356213, 14 pages
https://doi.org/10.1155/2017/7356213
Research Article

Collaborative QoS Prediction for Mobile Service with Data Filtering and SlopeOne Model

1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
2Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education, Hangzhou, Zhejiang 310027, China
3College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
4School of Software, Xidian University, Xi’an, Shanxi 710071, China
5Hithink RoyalFlush Information Network Co., Ltd., Hangzhou, Zhejiang, China

Correspondence should be addressed to Yueshen Xu; nc.ude.naidix@uxsy

Received 25 January 2017; Accepted 21 March 2017; Published 22 June 2017

Academic Editor: Jaegeol Yim

Copyright © 2017 Yuyu Yin 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

The mobile service is a widely used carrier for mobile applications. With the increase of the number of mobile services, for service recommendation and selection, the nonfunctional properties (also known as quality of service, QoS) become increasingly important. However, in many cases, the number of mobile services invoked by a user is quite limited, which leads to the large number of missing QoS values. In recent years, many prediction algorithms, such as algorithms extended from collaborative filtering (CF), are proposed to predict QoS values. However, the ideas of most existing algorithms are borrowed from the recommender system community, not specific for mobile service. In this paper, we first propose a data filtering-extended SlopeOne model (filtering-based CF), which is based on the characteristics of a mobile service and considers the relation with location. Also, using the data filtering technique in FB-CF and matrix factorization (MF), this paper proposes another model FB-MF (filtering-based MF). We also build an ensemble model, which combines the prediction results of FB-CF model and FB-MF model. We conduct sufficient experiments, and the experimental results demonstrate that our models outperform all compared methods and achieve good results in high data sparsity scenario.