Recommending collaborators using keywords

Sara Cohen, Lior Ebel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

This paper studies the problem of recommending collabora-tors in a social network, given a set of keywords. Formally, given a query q, consisting of a researcher s (who is a mem-ber of a social network) and a set of keywords k (e.g., an article name or topic of future work), the collaborator rec-ommendation problem is to return a high-quality ranked list of possible collaborators for s on the topic k. Extensive efiort was expended to define ranking functions that take into consideration a variety of properties, including struc-tural proximity to s, textual relevance to k, and importance. The efiectiveness of our methods have been experimentally proven over two large subsets of the social network deter-mined by DBLP co-authorship data. The results show that the ranking methods developed in this paper work well in practice.

Original languageAmerican English
Title of host publicationWWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web
PublisherAssociation for Computing Machinery
Pages959-962
Number of pages4
ISBN (Print)9781450320382
DOIs
StatePublished - 2013
EventWWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web - Rio de Janeiro, Brazil
Duration: 13 May 201317 May 2013

Publication series

NameWWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web

Conference

ConferenceWWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web
Country/TerritoryBrazil
CityRio de Janeiro
Period13/05/1317/05/13

Keywords

  • Collaborator recommendation
  • Social network

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