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

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

1 Citation (Scopus)

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
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

Other

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

Fingerprint

Recommender systems
learning
community
Experiments
experiment

All Science Journal Classification (ASJC) codes

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

Cite this

Yu, M., Zhang, X., & Kreager, D. (2016). New to online dating? Learning from experienced users for a successful match. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 467-470). [7752276] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752276
Yu, Mo ; Zhang, Xiaolong ; Kreager, Derek. / New to online dating? Learning from experienced users for a successful match. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 467-470
@inproceedings{f7abb8397aab41a1b6503dee2a4ba7f8,
title = "New to online dating? Learning from experienced users for a successful match",
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.",
author = "Mo Yu and Xiaolong Zhang and Derek Kreager",
year = "2016",
month = "11",
day = "21",
doi = "10.1109/ASONAM.2016.7752276",
language = "English (US)",
pages = "467--470",
booktitle = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Yu, M, Zhang, X & Kreager, D 2016, New to online dating? Learning from experienced users for a successful match. in Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752276, Institute of Electrical and Electronics Engineers Inc., pp. 467-470, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 8/18/16. https://doi.org/10.1109/ASONAM.2016.7752276

New to online dating? Learning from experienced users for a successful match. / Yu, Mo; Zhang, Xiaolong; Kreager, Derek.

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 467-470 7752276.

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

TY - GEN

T1 - New to online dating? Learning from experienced users for a successful match

AU - Yu, Mo

AU - Zhang, Xiaolong

AU - Kreager, Derek

PY - 2016/11/21

Y1 - 2016/11/21

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85006742082&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006742082&partnerID=8YFLogxK

U2 - 10.1109/ASONAM.2016.7752276

DO - 10.1109/ASONAM.2016.7752276

M3 - Conference contribution

AN - SCOPUS:85006742082

SP - 467

EP - 470

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Yu M, Zhang X, Kreager D. New to online dating? Learning from experienced users for a successful match. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 467-470. 7752276 https://doi.org/10.1109/ASONAM.2016.7752276