Authors:
Bruno Coelho
1
;
Fernando Costa
2
and
Gil M. Gonçalves
3
Affiliations:
1
INOVA+ and Centro de Inovação de Matosinhos, Portugal
;
2
Superior Institute of Engineering of Porto, Portugal
;
3
INOVA+, Centro de Inovação de Matosinhos, Faculty of Engineering and University of Porto, Portugal
Keyword(s):
recommender systems, decision support systems, match-making algorithms, jobs, employment, work, teams, user modelling, content-based filtering, collaborative filtering
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Process Management
;
Business-It Alignment
;
Cloud Computing
;
Collaboration and e-Services
;
Collaborative Systems
;
Complex Systems Modeling and Simulation
;
Data Communication Networking
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Logistics
;
Modeling and Frameworks
;
Platforms and Applications
;
Simulation and Modeling
;
Social Networks
;
Symbolic Systems
;
Telecommunications
Abstract:
Nowadays people search job opportunities or candidates mainly online, where several websites for this
purpose already do exist (LinkedIn, Guru and oDesk, amongst others). This task is especially difficult because
of the large number of items to look for and manual compatibility verification. What we propose in this paper
is a Hybrid Job Recommendation System that considers the user model (content-based filtering) and social
interactions (collaborative filtering) to improve the quality of its recommendations. Our solution is also able
to generate adequate teams for a given job opportunity, based not only on the needed competences but also
on the social compatibility between their members.