Fab: content-based, collaborative recommendation
Communications of the ACM
Automated Collaborative Filtering Applications for Online Recruitment Services
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Matching People and Jobs: A Bilateral Recommendation Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Decision support for team staffing: An automated relational recommendation approach
Decision Support Systems
| Hi-index | 0.00 |
In this paper, a hybrid recommender system for job seeking and recruiting websites is presented. The various interaction features designed on the website help the users organize the resources they need as well as express their interest. The hybrid recommender system exploits the job and user profiles and the actions undertaken by users in order to generate personalized recommendations of candidates and jobs. The data collected from the website is modeled using a directed, weighted, and multi-relational graph, and the 3A ranking algorithm is exploited to rank items according to their relevance to the target user. A preliminary evaluation is conducted based on simulated data and production data from a job hunting website in Switzerland.