Chala, Sisay Adugna: Bidirectional job matching through unsupervised feature learning. 2017
Inhalt
- Acknowledgements
- Abstract
- Zusammenfassung
- Contents
- List of Figures
- List of Tables
- List of Acronyms
- 1 Introduction and Background
- 1.1 Background
- 1.2 Problem Statement
- 1.3 Research Question
- 1.4 Research Goals and Objectives
- 1.5 Significance and Contributions of the Research
- 1.6 Methods and Procedures
- 1.7 Structure of the Dissertation
- 2 Review of Related Works
- 2.1 Occupational Information Systems
- 2.2 Dynamic Interfaces for Job Seeker Data Collection
- 2.3 Social Network Analysis for Job Seeker Modeling
- 2.4 Online Vacancy Mining and Modeling
- 2.5 Job Seeker and Vacancy Matching
- 2.6 Online Job Matching Systems
- 3 Theoretical and Conceptual Foundation
- 3.1 Job Matching
- 3.2 Enriching Vacancies with Occupational Standards
- 3.3 Knowledge Based Methods in Job Matching
- 3.4 Machine Learning and Natural Language Processing
- 3.5 Deep Learning with Convolutional Neural Networks (CNN)
- 3.6 NLP for Data-intensive Job Matching
- 3.7 Conception of Bidirectional Job Matching
- 4 Job Seeker Analysis and Modeling
- 4.1 Job Seeker Data Collection and Integration
- 4.2 Job Seeker Analysis and Modeling
- 4.3 DTF for Self-assessment Survey
- 4.4 Job Seeker Analysis with Social Network
- 4.5 Measuring Job Seeker Skill
- 5 Job Vacancy Analysis and Modeling
- 6 Matching Job Vacancies to Job Seekers
- 6.1 Bidirectional Matching of Job Seeker to Vacancy
- 6.2 Estimating Similarity between Job Seeker and Vacancies
- 6.3 Recommendation Processes
- 7 Experimental Result and Evaluation
- 7.1 Results and Discussion
- 7.1.1 DTF-enabled Context-aware User Interface
- 7.1.2 Bidirectional Candidate to Vacancy Matching
- 7.1.3 Inclusion of Social Networking Data
- 7.2 Contributions
- 8 Conclusion and Future Works
- Bibliography