NP-complete stable matching problems
Journal of Algorithms
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
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
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Approximating Matches Made in Heaven
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
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
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Relevance and ranking in online dating systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
People recommendation based on aggregated bidirectional intentions in social network site
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
When LP is the cure for your matching woes: improved bounds for stochastic matchings
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part II
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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
Explicit and implicit user preferences in online dating
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
A people-to-people recommendation system using tensor space models
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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
Who likes it more?: mining worth-recommending items from long tails by modeling relative preference
Proceedings of the 7th ACM international conference on Web search and data mining
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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.