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

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...

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Bibliographic Details
Published in:Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-8
Main Authors: Wang, Xuzhong, Nan, Yu, Liu, Yan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Datasets
Game theory
Joining
Knowledge
Linking
Machine learning
Markets
Matching
Mathematical analysis
Methods
Networks
R&D
Recommender systems
Research & development
Similarity
Social networks
Studies
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2017/3247627