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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive radio: achieving consensus using negative preferences
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
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
CCR: a content-collaborative reciprocal recommender for online dating
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MEET: a generalized framework for reciprocal recommender systems
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iHR: an online recruiting system for Xiamen Talent Service Center
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User Modeling and User-Adapted Interaction
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This paper explores ways to address the problem of the high cost problem of poor recommendations in reciprocal recommender systems. These systems recommend one person to another and require that both people like each other for the recommendation to be successful. A notable example, and the focus of our experiments is online dating. In such domains, poor recommendations should be avoided as they cause users to suffer repeated rejection and abandon the site. This paper describes our experiments to create a recommender based on two classes of models: one to predict who each user will like; the other to predict who each user will dislike. We then combine these models to generate recommendations for the user. This work is novel in exploring modelling both people's likes and dislikes and how to combine these to support a reciprocal recommendation, which is important for many domains, including online dating, employment, mentor-mentee matching and help-helper matching. Using a negative and a positive preference model in a combined manner, we improved the success rate of reciprocal recommendations by 18% while, at the same time, reducing the failure rate by 36% for the top-1 recommendations in comparison to using the positive model of preference alone.