Finding cut from the same cloth: Cross network link recommendation via joint matrix factorization

Arun Reddy Nelakurthi, Jingrui He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

With the emergence of online forums associated with major diseases, such as diabetes mellitus, many patients are increasingly dependent on such disease-specific social networks to gain access to additional resources. Among these patients, it is common for them to stick to one disease-specific social network, although their desired resources might be spread over multiple social networks, such as patients with similar questions and concerns. Motivated by this application, in this paper, we focus on cross network link recommendation, which aims to identify similar users across multiple heterogeneous social networks. The problem setting is different from existing work on cross network link prediction, which either tries to link accounts of the same user from different social networks, or aims to match users with complementary expertise or interest. To approach the problem of cross network link recommendation, we propose to jointly decompose the user-keyword matrices from multiple social networks, while requiring them to share the same topics and user group-topic association matrices. This constraint comes from the fact that social networks dedicated to the same disease tend to share the same topics as well as the interests of users groups in certain topics. Based on this intuition, we construct a generic optimization framework, provide four instantiations and an iterative optimization algorithm with performance analysis. In the experiments, we demonstrate the superiority of the proposed algorithm over state-of-the-art techniques on various real-world data sets.

Original languageEnglish (US)
Title of host publication31st AAAI Conference on Artificial Intelligence, AAAI 2017
PublisherAAAI press
Pages1467-1473
Number of pages7
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017

Other

Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period2/4/172/10/17

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Factorization
Medical problems
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Nelakurthi, A. R., & He, J. (2017). Finding cut from the same cloth: Cross network link recommendation via joint matrix factorization. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 1467-1473). AAAI press.

Finding cut from the same cloth : Cross network link recommendation via joint matrix factorization. / Nelakurthi, Arun Reddy; He, Jingrui.

31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, 2017. p. 1467-1473.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nelakurthi, AR & He, J 2017, Finding cut from the same cloth: Cross network link recommendation via joint matrix factorization. in 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, pp. 1467-1473, 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, United States, 2/4/17.
Nelakurthi AR, He J. Finding cut from the same cloth: Cross network link recommendation via joint matrix factorization. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press. 2017. p. 1467-1473
Nelakurthi, Arun Reddy ; He, Jingrui. / Finding cut from the same cloth : Cross network link recommendation via joint matrix factorization. 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, 2017. pp. 1467-1473
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