Make new friends, but keep the old - Recommending people on social networking sites

Jilin Chen, Werner Geyer, Casey Dugan, Michael Muller, Ido Guy

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

335 Scopus citations

Abstract

This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users' friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.

Original languageEnglish
Title of host publicationCHI 2009
Subtitle of host publicationDigital Life New World - Proceedings of the 27th International Conference on Human Factors in Computing Systems
Pages201-210
Number of pages10
DOIs
StatePublished - 1 Dec 2009
Externally publishedYes
Event27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009 - Boston, MA, United States
Duration: 4 Apr 20099 Apr 2009

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009
Country/TerritoryUnited States
CityBoston, MA
Period4/04/099/04/09

Keywords

  • Friend
  • Recommender system
  • Social networking

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