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Mathematical Problems in Engineering
Volume 2017, Article ID 3247627, 8 pages
https://doi.org/10.1155/2017/3247627
Research Article

A Stable-Matching-Based User Linking Method with User Preference Order

China State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002, China

Correspondence should be addressed to Yan Liu; moc.nuyila@nayuil_sm

Received 10 December 2016; Revised 18 February 2017; Accepted 1 March 2017; Published 28 March 2017

Academic Editor: Zonghua Zhang

Copyright © 2017 Xuzhong Wang 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

With the development of social networks, more and more users choose to use multiple accounts from different networks to meet their needs. Linking a particular user’s multiple accounts not only can improve user’s experience of the net-services such as recommender system, but also plays a significant role in network security. However, multiple accounts of the same user are often not directly linked to each other, and further, the privacy policy provided by the service provider makes it harder to find accounts for a particular user. In this paper, we propose a stable-matching-based method with user preference order for the problem of low accuracy of user linking in cross-media sparse data. Different from the traditional way which just calculates the similarity of accounts, we take full account of the mutual influence among multiple accounts by regarding different networks as bilateral (multilateral) market and user linking as a stable matching problem in such a market. Based on the combination of Game-Theoretic Machine Learning and Pairwise, a novel user linking method has been proposed. The experiment shows that our method has a 21.6% improvement in accuracy compared with the traditional linking method and a further increase of about 7.8% after adding the prior knowledge.