article

Recommending people to people: the nature of reciprocal recommenders with a case study in online dating

Authors:

Joshua Akehurst,

Irena Koprinska,

Judy KayAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 23, Issue 5

Pages 447 - 488

https://doi.org/10.1007/s11257-012-9125-0

Published: 01 November 2013 Publication History

Abstract

People-to-people recommenders constitute an important class of recommender systems. Examples include online dating, where people have the common goal of finding a partner, and employment websites where one group of users needs to find a job (employer) and another group needs to find an employee. People-to-people recommenders differ from the traditional items-to-people recommenders as they must satisfy both parties; we call this type of recommender reciprocal. This article is the first to present a comprehensive view of this important recommender class. We first identify the characteristics of reciprocal recommenders and compare them with traditional recommenders, which are widely used in e-commerce websites. We then present a series of studies and evaluations of a content-based reciprocal recommender in the domain of online dating. It uses a large dataset from a major online dating website. We use this case study to illustrate the distinctive requirements of reciprocal recommenders and highlight important challenges, such as the need to avoid bad recommendations since they may make users to feel rejected. Our experiments indicate that, by considering reciprocity, the rate of successful connections can be significantly improved. They also show that, despite the existence of rich explicit profiles, the use of implicit profiles provides more effective recommendations. We conclude with a discussion, linking our work in online dating to the many other domains that require reciprocal recommenders. Our key contributions are the recognition of the reciprocal recommender as an important class of recommender, the identification of its distinctive characteristics and the exploration of how these impact the recommendation process in an extensive case study in the domain of online dating.

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Cited By

Yang CDai SHou YZhao WXu JSong YZhu HBaeza-Yates RBonchi F(2024)Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and MethodProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671734(3714-3723)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671734
Quelhas AOka NTanaka K(2024)Exploring Interest Similarity Features and Their Combinations for Friendship Recommendation Without Cold StartCollaboration Technologies and Social Computing10.1007/978-3-031-67998-8_13(195-202)Online publication date: 11-Sep-2024
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Ranjbar Kermany NPizzato LMin TScott CLeontjeva A(2022)A Multi-Stakeholder Recommender System for Rewards RecommendationsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547388(484-487)Online publication date: 12-Sep-2022
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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 23, Issue 5

November 2013

81 pages

ISSN:0924-1868

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Copyright © Copyright © 2013 Springer Science+Business Media Dordrecht.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2013

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Yang CDai SHou YZhao WXu JSong YZhu HBaeza-Yates RBonchi F(2024)Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and MethodProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671734(3714-3723)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671734
Quelhas AOka NTanaka K(2024)Exploring Interest Similarity Features and Their Combinations for Friendship Recommendation Without Cold StartCollaboration Technologies and Social Computing10.1007/978-3-031-67998-8_13(195-202)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-67998-8_13
Ranjbar Kermany NPizzato LMin TScott CLeontjeva A(2022)A Multi-Stakeholder Recommender System for Rewards RecommendationsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547388(484-487)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547388
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
Ceccarini CNisi VPrandi C(2022)Exploring proximity-based recommendation criteria as a tool for information exchange and interactions between locals and touristsMultimedia Tools and Applications10.1007/s11042-022-13369-y82:4(5229-5252)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1007/s11042-022-13369-y
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: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s00521-021-06749-2
Li RYe XZhou HZha H(2021)Learning to match via inverse optimal transportThe Journal of Machine Learning Research10.5555/3322706.336202120:1(2954-2990)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.5555/3322706.3362021
Mena-Maldonado ECañamares RCastells PRen YSanderson M(2021)Popularity Bias in False-positive Metrics for Recommender Systems EvaluationACM Transactions on Information Systems10.1145/345274039:3(1-43)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3452740
Gasparetti FSansonetti GMicarelli A(2021)Community detection in social recommender systems: a surveyApplied Intelligence10.1007/s10489-020-01962-351:6(3975-3995)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10489-020-01962-3
Palomares IHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Reciprocal Recommendation: Matching Users with the Right UsersProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401420(2429-2431)Online publication date: 25-Jul-2020
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