Utility-Based Link Recommendation for Online Social Networks

Zhepeng Li (), Xiao Fang (), Xue Bai () and Olivia R. Liu Sheng ()
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Zhepeng Li: Schulich School of Business, York University, Toronto, Ontario M3J 1P3, Canada
Xiao Fang: Lerner College of Business and Economics, University of Delaware, Newark, Delaware 19716
Xue Bai: School of Business, University of Connecticut, Storrs, Connecticut 06268
Olivia R. Liu Sheng: David Eccles School of Business, University of Utah, Salt Lake City, Utah 84112

Management Science, 2017, vol. 63, issue 6, 1938-1952

Abstract: Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networks. Salient examples of link recommendation include “People You May Know” on Facebook and LinkedIn as well as “You May Know” on Google+. The main stakeholders of an online social network include users (e.g., Facebook users) who use the network to socialize with other users and an operator (e.g., Facebook Inc.) that establishes and operates the network for its own benefit (e.g., revenue). Existing link recommendation methods recommend links that are likely to be established by users but overlook the benefit a recommended link could bring to an operator. To address this gap, we define the utility of recommending a link and formulate a new research problem—the utility-based link recommendation problem. We then propose a novel utility-based link recommendation method that recommends links based on the value, cost, and linkage likelihood of a link, in contrast to existing link recommendation methods that focus solely on linkage likelihood. Specifically, our method models the dependency relationship between the value, cost, linkage likelihood, and utility-based link recommendation decision using a Bayesian network; predicts the probability of recommending a link with the Bayesian network; and recommends links with the highest probabilities. Using data obtained from a major U.S. online social network, we demonstrate significant performance improvement achieved by our method compared with prevalent link recommendation methods from representative prior research.

Keywords: utility-based link recommendation; link prediction; Bayesian network learning; continuous latent factor; online social network; machine learning; network formation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (10)

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