Matching People and Jobs: A Bilateral Recommendation Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Dependent rounding and its applications to approximation algorithms
Journal of the ACM (JACM)
Approximating Matches Made in Heaven
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Relevance and ranking in online dating systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Optimizing multiple objectives in collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
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
MEET: a generalized framework for reciprocal recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
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
User Modeling and User-Adapted Interaction
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The bias towards popular items is not necessarily an undesired outcome of recommender algorithms since a large amount of revenue on e-commerce websites is drawn from these popular items. On the other hand, in domains such as online dating and employment websites, where users and items of the recommendation are both people, a strong bias towards popular users may cause these users to feel overwhelmed and unpopular users to feel neglected. In this paper, we use collaborative filtering (CF) to generate recommendations for all users, and by using stochastic matching we select a number of reciprocal recommendations for each user that maximizes the matches among all users. In this way, all users, regardless of their popularity, will receive the same number of recommendations the number of times they will be recommended to others. This study is the first to apply a stochastic matching solution to balance the number of recommendations given to users in a CF setting. Using historical data, we demonstrate that the proposed recommender improves the chance of finding a successful relationship in comparison to CF recommendations.