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- Publisher Website: 10.1145/3131782
- Scopus: eid_2-s2.0-85033239979
- WOS: WOS:000426897600001
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Article: A survey of link recommendation for social networks: Methods, theoretical foundations, and future research directions
Title | A survey of link recommendation for social networks: Methods, theoretical foundations, and future research directions |
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Authors | |
Keywords | Network formation Link recommendation Social network |
Issue Date | 2018 |
Citation | ACM Transactions on Management Information Systems, 2018, v. 9, n. 1, article no. 1 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/302212 |
ISSN | 2023 Impact Factor: 2.5 2023 SCImago Journal Rankings: 0.815 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, Zhepeng | - |
dc.contributor.author | Fang, Xiao | - |
dc.contributor.author | Sheng, Olivia R.Liu | - |
dc.date.accessioned | 2021-08-30T13:58:01Z | - |
dc.date.available | 2021-08-30T13:58:01Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | ACM Transactions on Management Information Systems, 2018, v. 9, n. 1, article no. 1 | - |
dc.identifier.issn | 2158-656X | - |
dc.identifier.uri | http://hdl.handle.net/10722/302212 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | ACM Transactions on Management Information Systems | - |
dc.subject | Network formation | - |
dc.subject | Link recommendation | - |
dc.subject | Social network | - |
dc.title | A survey of link recommendation for social networks: Methods, theoretical foundations, and future research directions | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1145/3131782 | - |
dc.identifier.scopus | eid_2-s2.0-85033239979 | - |
dc.identifier.volume | 9 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | article no. 1 | - |
dc.identifier.epage | article no. 1 | - |
dc.identifier.eissn | 2158-6578 | - |
dc.identifier.isi | WOS:000426897600001 | - |