Fighting Information Overflow with Personalized Comprehensive Information Access: A Proactive Job Recommender

Authors:
Danielle H. Lee;Peter Brusilovsky
Affiliations:
University of Pittsburgh, USA;University of Pittsburgh, USA
Venue:
ICAS '07 Proceedings of the Third International Conference on Autonomic and Autonomous Systems
Year:
2007

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Abstract

Searching for jobs online is an information intensive activity, because thousands of jobs are posted on the Web daily and it takes a great deal of effort to find the right position. Job search sites require recommender systems to meet diversified information needs: Job seekers who have well-defined careers try to focus on relevant open positions while students who have general and evolving interests want to follow the dominant trends of the job market in order to plan their career path. In this paper, we introduce a comprehensive job recommender system. From the user's perspective, four different kinds of recommendations are implemented. Users of this system can retrieve open jobs with different methods, ranging from exploring to searching.