GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Proceedings of the 6th international conference on Intelligent user interfaces
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
User Modelling in I-Help: What, Why, When and How
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Homophily in online dating: when do you like someone like yourself?
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
Matching People and Jobs: A Bilateral Recommendation Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Expertise Recommendation: A Two-Way Knowledge Communication Channel
ICAS '08 Proceedings of the Fourth International Conference on Autonomic and Autonomous Systems
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Increasing engagement through early recommender intervention
Proceedings of the third ACM conference on Recommender systems
Rate it again: increasing recommendation accuracy by user re-rating
Proceedings of the third ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
Finding someone you will like and who won't reject you
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Stochastic matching and collaborative filtering to recommend people to people
Proceedings of the fifth ACM conference on Recommender systems
CCR: a content-collaborative reciprocal recommender for online dating
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
The effect of suspicious profiles on people recommenders
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Online dating recommender systems: the split-complex number approach
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
Proceedings of the sixth ACM conference on Recommender systems
MEET: a generalized framework for reciprocal recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
An Adaptive Match-Making System reflecting the explicit and implicit preferences of users
Expert Systems with Applications: An International Journal
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
People-to-People recommendation using multiple compatible subgroups
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
iHR: an online recruiting system for Xiamen Talent Service Center
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A people-to-people content-based reciprocal recommender using hidden markov models
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM conference on Recommender systems
CLiMF: collaborative less-is-more filtering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
User Modeling and User-Adapted Interaction
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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.