Unifying Online and Offline Preference for Social Link Prediction

Fan Zhou (), Kunpeng Zhang (), Bangying Wu (), Yi Yang () and Harry Jiannan Wang ()
Additional contact information
Fan Zhou: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
Kunpeng Zhang: Department of Decision Operations & Information Technologies, University of Maryland, College Park; College Park, Maryland 20742
Bangying Wu: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
Yi Yang: Department of Information Systems, Business Statistics and Operations Management (ISOM), Hong Kong University of Science and Technology, Hong Kong
Harry Jiannan Wang: Department of Management Information Systems, University of Delaware, Newark, Delaware 19716

INFORMS Journal on Computing, 2021, vol. 33, issue 4, 1400-1418

Abstract: Recent advances in network representation learning have enabled significant improvement in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data become available because of the development of location-based technologies, we argue that this resourceful mobility data can be used to improve link prediction tasks. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via a probabilistic factor model and represent user social relations using neural network representation learning. To capture the interrelationship of these two sources, we develop an anchor link method to align these two different user latent representations. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also improves the efficiency of convolutional network learning. By comparing with several baseline methods that solely rely on social networks or mobility data, we show that our unified approach significantly improves the link prediction performance. Summary of Contribution: This paper proposes a novel framework that utilizes both user offline and online behavior for social link prediction by developing several machine learning algorithms, such as probabilistic factor model, neural network embedding, anchor link model, and locality-sensitive hashing. The scope and mission has the following aspects: (1) We develop a data and knowledge modeling approach that demonstrates significant performance improvement. (2) Our method can efficiently manage large-scale data. (3) We conduct rigorous experiments on real-world data sets and empirically show the effectiveness and the efficiency of our proposed method. Overall, our paper can contribute to the advancement of social link prediction, which can spur many downstream applications in information systems and computer science.

Keywords: link prediction; location-based learning; network representation learning; anchor link; locality sensitive hashing (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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