research-article

Online dating recommendations: matching markets and learning preferences

Authors:

Xiaodong WangAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 787 - 792

https://doi.org/10.1145/2567948.2579240

Published: 07 April 2014 Publication History

Abstract

Recommendation systems for online dating have recently attracted much attention from the research community. In this paper we propose a two-side matching framework for online dating recommendations and design an Latent Dirichlet Allocation (LDA) model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large online dating website shows that two-sided matching improves the rate of successful matches by as much as 45%. Finally, using simulated matching, we show that the LDA model can correctly capture user preferences.

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Cited By

Olawe O(2024)Analyzing Interaction Patterns on Dating Sites: A Generic Structure Potential ApproachProfessional Discourse & Communication10.24833/2687-0126-2024-6-3-60-796:3(60-79)Online publication date: 23-Sep-2024
https://doi.org/10.24833/2687-0126-2024-6-3-60-79
Shi P(2024)The Welfare Effects of Selling Leads in a Two-Sided MarketplaceSSRN Electronic Journal10.2139/ssrn.4727198Online publication date: 2024
https://doi.org/10.2139/ssrn.4727198
Kang CChoi YSohn YLim HHong H(2024)Beyond Swipes and Scores: Investigating Practices, Challenges and User-Centered Values in Online Dating AlgorithmsProceedings of the ACM on Human-Computer Interaction10.1145/36870258:CSCW2(1-30)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687025
Show More Cited By

Index Terms

Online dating recommendations: matching markets and learning preferences
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Other conferences

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 ACM.

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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WWW '14

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IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Olawe O(2024)Analyzing Interaction Patterns on Dating Sites: A Generic Structure Potential ApproachProfessional Discourse & Communication10.24833/2687-0126-2024-6-3-60-796:3(60-79)Online publication date: 23-Sep-2024
https://doi.org/10.24833/2687-0126-2024-6-3-60-79
Shi P(2024)The Welfare Effects of Selling Leads in a Two-Sided MarketplaceSSRN Electronic Journal10.2139/ssrn.4727198Online publication date: 2024
https://doi.org/10.2139/ssrn.4727198
Kang CChoi YSohn YLim HHong H(2024)Beyond Swipes and Scores: Investigating Practices, Challenges and User-Centered Values in Online Dating AlgorithmsProceedings of the ACM on Human-Computer Interaction10.1145/36870258:CSCW2(1-30)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687025
Park SSong JKarger DMalone T(2024)Who2chat: A Social Networking System for Academic Researchers in Virtual Social Hours Enabling Coordinating, Overcoming Barriers and Social SignalingProceedings of the ACM on Human-Computer Interaction10.1145/36374358:CSCW1(1-34)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637435
Fang AZhu H(2022)Matching for Peer Support: Exploring Algorithmic Matching for Online Mental Health CommunitiesProceedings of the ACM on Human-Computer Interaction10.1145/35552026:CSCW2(1-37)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555202
Su YBayoumi MJoachims T(2022)Optimizing Rankings for Recommendation in Matching MarketsProceedings of the ACM Web Conference 202210.1145/3485447.3511961(328-338)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3511961
Zheng XZhao GZhu LZhu JQian X(2022)What You Like, What I Am: Online Dating Recommendation via Matching Individual Preferences with FeaturesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3148485(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3148485
Luo LZhang XChen XLiu KPeng DYang X(2022)DCRS: a deep contrast reciprocal recommender system to simultaneously capture user interest and attractiveness for online datingNeural Computing and Applications10.1007/s00521-021-06749-234:8(6413-6425)Online publication date: 17-Jan-2022
https://doi.org/10.1007/s00521-021-06749-2
Pidoux JKuntz PGatica-Perez D(2021)Declarative Variables in Online DatingProceedings of the ACM on Human-Computer Interaction10.1145/34491745:CSCW1(1-32)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449174
Ionescu SHannák AJoseph K(2021)An Agent-based Model to Evaluate Interventions on Online Dating Platforms to Decrease Racial HomogamyProceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency10.1145/3442188.3445904(412-423)Online publication date: 3-Mar-2021
https://dl.acm.org/doi/10.1145/3442188.3445904
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