Authors:
Giacomo Domeniconi
;
Gianluca Moro
;
Andrea Pagliarani
;
Karin Pasini
and
Roberto Pasolini
Affiliation:
Università degli Studi di Bologna, Italy
Keyword(s):
Job Seeking, Hierarchical Clustering, Latent Semantic Analysis, Recruiting, Recruitment.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Clustering
;
Economics, Business and Forecasting Applications
;
Information Retrieval and Learning
;
Matrix Factorization
;
Pattern Recognition
;
Theory and Methods
Abstract:
Until recently job seeking has been a tricky, tedious and time consuming process, because people looking for a new position had to collect information from many different sources. Job recommendation systems have been proposed in order to automate and simplify this task, also increasing its effectiveness. However, current approaches rely on scarce manually collected data that often do not completely reveal people skills. Our work aims to find out relationships between jobs and people skills making use of data from LinkedIn users’ public profiles. Semantic associations arise by applying Latent Semantic Analysis (LSA). We use the mined semantics to obtain a hierarchical clustering of job positions and to build a job recommendation system. The outcome proves the effectiveness of our method in recommending job positions. Anyway, we argue that our approach is definitely general, because the extracted semantics could be worthy not only for job recommendation systems but also for recruiting
systems. Furthermore, we point out that both the hierarchical clustering and the recommendation system do not require parameters to be tuned.
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