Affiliation:
1. York University, Ontario, Canada
2. University of Delaware, Newark, Delaware
3. University of Utah, Salt Lake City, Utah
Abstract
Link recommendation has attracted significant attention from both industry practitioners and academic researchers. In industry, link recommendation has become a standard and most important feature in online social networks, prominent examples of which include “People You May Know” on LinkedIn and “You May Know” on Google+. In academia, link recommendation has been and remains a highly active research area. This article surveys state-of-the-art link recommendation methods, which can be broadly categorized into learning-based methods and proximity-based methods. We further identify social and economic theories, such as social interaction theory, that underlie these methods and explain from a theoretical perspective why a link recommendation method works. Finally, we propose to extend link recommendation research in several directions that include utility-based link recommendation, diversity of link recommendation, link recommendation from incomplete data, and experimental study of link recommendation.
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Management Information Systems
Cited by
43 articles.
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