C4.5: programs for machine learning
C4.5: programs for machine learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Automated Collaborative Filtering Applications for Online Recruitment Services
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Efficient query expansion with auxiliary data structures
Information Systems
Automatic Profiling System for Ranking Candidates Answers in Human Resources
OTM '08 Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS
E-Gen: automatic job offer processing system for human resources
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Pruning terminology extracted from a specialized corpus for CV ontology acquisition
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part II
A personality mining system for automated applicant ranking in online recruitment systems
ICWE'11 Proceedings of the 11th international conference on Web engineering
Automating competence management through non-standard reasoning
Engineering Applications of Artificial Intelligence
Domain driven data mining in human resource management: A review of current research
Expert Systems with Applications: An International Journal
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The market of online job search sites grows exponentially. This implies volumes of information (mostly in the form of free text) become manually impossible to process. An analysis and assisted categorization seems relevant to address this issue. We present E-Gen, a system which aims to perform assisted analysis and categorization of job offers and of the responses of candidates. This paper presents several strategies based on vectorial and probabilistic models to solve the problem of profiling applications according to a specific job offer. Our objective is a system capable of reproducing the judgement of the recruitment consultant. We have evaluated a range of measures of similarity to rank candidatures by using ROC curves. Relevance feedback approach allows to surpass our previous results on this task, difficult, diverse and higly subjective.