research-article

RECON: a reciprocal recommender for online dating

Authors:

Irena Koprinska,

Judy KayAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 207 - 214

https://doi.org/10.1145/1864708.1864747

Published: 26 September 2010 Publication History

Abstract

The reciprocal recommender is a class of recommender system that is important for several tasks where people are both the subjects and objects of the recommendation. Some examples are: job recommendation, mentor-mentee matching, and online dating. Despite the importance of this type of recommender, our work is the first to distinguish it and define its properties. We have implemented RECON, a reciprocal recommender for online dating, and have evaluated it on a large dataset from a major Australian dating website. We investigated the predictive power gained by taking account of reciprocity, finding that it is substantial, for example it improved the success rate of the top ten recommendations from 23% to 42% and also improved the recall at the same time. We also found reciprocity to help with the cold start problem obtaining a success rate of 26% for the top ten recommendations for new users. We discuss the implications of these results for broader uses of our approach for other reciprocal recommenders.

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Cited By

Musick GDuan WNajafian SSengupta SFlathmann CKnijnenburg BMcNeese N(2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3633074
Ya JDeng ZGao H(2024)Enhancing Reciprocal Recommendation with Bidirectional Global-Local Insights2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)10.1109/CCAI61966.2024.10603077(314-319)Online publication date: 24-May-2024
https://doi.org/10.1109/CCAI61966.2024.10603077
Bojic L(2024)AI alignment: Assessing the global impact of recommender systemsFutures10.1016/j.futures.2024.103383160(103383)Online publication date: Jun-2024
https://doi.org/10.1016/j.futures.2024.103383
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Index Terms

RECON: a reciprocal recommender for online dating
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Many social networks in our daily life are bipartite networks that are built on reciprocity. How can we recommend users/friends to a user, so that the user is interested in and attractive to recommended users? In this research, we propose a new ...

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Published In

cover image ACM Conferences

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 September 2010

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RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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RecSys '24

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October 14 - 18, 2024

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Cited By

Musick GDuan WNajafian SSengupta SFlathmann CKnijnenburg BMcNeese N(2024)To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/36330748:GROUP(1-28)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3633074
Ya JDeng ZGao H(2024)Enhancing Reciprocal Recommendation with Bidirectional Global-Local Insights2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)10.1109/CCAI61966.2024.10603077(314-319)Online publication date: 24-May-2024
https://doi.org/10.1109/CCAI61966.2024.10603077
Bojic L(2024)AI alignment: Assessing the global impact of recommender systemsFutures10.1016/j.futures.2024.103383160(103383)Online publication date: Jun-2024
https://doi.org/10.1016/j.futures.2024.103383
Jannach DAbdollahpouri H(2023)A survey on multi-objective recommender systemsFrontiers in Big Data10.3389/fdata.2023.11578996Online publication date: 22-Mar-2023
https://doi.org/10.3389/fdata.2023.1157899
Tomita YTogashi RHashizume YOhsaka N(2023)Fast and Examination-agnostic Reciprocal Recommendation in Matching MarketsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608774(12-23)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608774
Kumari TSharma RGupta BBedi P(2023)Generating Popularity-Aware Reciprocal Recommendations Using Siamese Bi-Directional Gated Recurrent Units NetworkVietnam Journal of Computer Science10.1142/S219688882350004510:03(273-301)Online publication date: 31-May-2023
https://doi.org/10.1142/S2196888823500045
Zeng WZhao XTan ZTang JCheng X(2023)Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental StudyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327258435:12(12770-12784)Online publication date: 1-Dec-2023
https://doi.org/10.1109/TKDE.2023.3272584
Nobuaki KSatoshi F(2023)DODA: A Decentralized Online Dating Application2023 Eleventh International Symposium on Computing and Networking Workshops (CANDARW)10.1109/CANDARW60564.2023.00061(323-327)Online publication date: 27-Nov-2023
https://doi.org/10.1109/CANDARW60564.2023.00061
Zhang LWang XYamasaki T(2023)Graph Fusion in Reciprocal Recommender SystemsIEEE Access10.1109/ACCESS.2023.323978511(8860-8869)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3239785
Shaheen JHenley CMcKenna LHoang SAbdulwahab F(2022)Comparative Agent-Based Simulations on Levels of Multiplicity Using a Network Regression: A Mobile Dating Use-CaseApplied Sciences10.3390/app1204198212:4(1982)Online publication date: 14-Feb-2022
https://doi.org/10.3390/app12041982
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