People-to-People Reciprocal Recommenders

Abstract

People-to-people reciprocal recommenders are an emerging class of recommender systems. They differ from traditional items-to-people recommenders as they must satisfy the preferences and needs of the two parties involved in the recommendation. In contrast, traditional items-to-people recommenders are one-sided and must satisfy only the preference of the person for whom the recommendation is generated. We review the characteristics and present an overview of existing reciprocal recommenders. To illustrate the various aspects of these recommenders and how reciprocity can be taken into account in building and evaluating such recommenders, we present a case study in online dating. We describe our reciprocal recommender algorithm that combines content-based and collaborative filtering and uses data from both user profiles and user interactions. We also study the differences between the implicit and explicit user preferences and show that implicit preferences, learned from user interactions, are better predictors of successful interactions. We conclude by outlining some future research directions.

Notes

Acknowledgements

This work was supported by the Smart Services Cooperative Research Centre. We also thank Joshua Akehurst, Luiz Pizzato and Judy Kay for their contributions to this work.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia

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