New to online dating? Learning from experienced users for a successful match

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Online dating arises as a popular venue for finding romantic partners in recent years. Many online dating sites adopt recommender systems to help their users. However, few of current research provides solutions to cold start problem, i.e., providing recommendations to new users. In this research, we propose a new approach of providing reciprocal online dating recommendations to new users. Specifically, we detect communities from existing users, match new users to these communities, and take advantage of reciprocal activities of those community members to provide recommendations to new users. Using data from a popular U.S. online dating site, experiments show that our approach greatly outperforms existing methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
EditorsRavi Kumar, James Caverlee, Hanghang Tong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages467-470
Number of pages4
ISBN (Electronic)9781509028467
DOIs
StatePublished - Nov 21 2016
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: Aug 18 2016Aug 21 2016

Publication series

NameProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Country/TerritoryUnited States
CitySan Francisco
Period8/18/168/21/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

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