Decision support for team staffing: An automated relational recommendation approach
Decision Support Systems
Proceedings of the special interest group on management information system's 47th annual conference on Computer personnel research
A social matching approach to support team configuration
CRIWG'09 Proceedings of the 15th international conference on Groupware: design, implementation, and use
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Machine learned job recommendation
Proceedings of the fifth ACM conference on Recommender systems
Stochastic matching and collaborative filtering to recommend people to people
Proceedings of the fifth ACM conference on Recommender systems
Online dating recommender systems: the split-complex number approach
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
Proceedings of the 13th International Conference on Computer Systems and Technologies
ResearchBroker: connecting researchers to real-world research opportunities
Proceedings of the 2013 conference on Computer supported cooperative work companion
A recommender system for job seeking and recruiting website
Proceedings of the 22nd international conference on World Wide Web companion
iHR: an online recruiting system for Xiamen Talent Service Center
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 7th ACM conference on Recommender systems
Identifying candidate datasets for data interlinking
ICWE'13 Proceedings of the 13th international conference on Web Engineering
User Modeling and User-Adapted Interaction
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Recommendation systems are widely used on the Internet to assist customers in finding the products or services that best fit with their individual preferences. While current implementations successfully reduce information overload by generating personalized suggestions when searching for objects such as books or movies, recommendation systems so far cannot be found in another potential field of application: the personalized search for subjects such as applicants in a recruitment scenario. Theory shows that a good match between persons and jobs needs to consider both, the preferences of the recruiter and the preferences of the candidate. Based on this requirement for modeling bilateral selection decisions, we present an approach applying two distinct recommendation systems to the field in order to improve the match between people and jobs. Finally, we present first validation test runs from a student experiment showing promising results.