NDLI: Exploration of methodologies to improve job recommender systems on social networks

Content Provider	Springer Nature Link
Author	Diaby, Mamadou Viennet, Emmanuel Launay, Tristan
Copyright Year	2014
Abstract	This paper presents content-based recommender systems which propose relevant jobs to Facebook and LinkedIn users. These systems have been developed at Work4, the Global Leader in Social and Mobile Recruiting. The profile of a social network user contains two types of data: user data and user friend data; furthermore, the profile of our users and the description of our jobs consist of text fields. The first experiments suggest that to predict the interests of users for jobs using basic similarity measures together with data collected by Work4 can be improved upon. The next experiments then propose a method to estimate the importance of users’ and jobs’ different fields in the task of job recommendation; taking into account these weights allow us to significantly improve the recommendations. The third part of this paper analyzes social recommendation approaches, validating the suitability for job recommendations for Facebook and LinkedIn users. The last experiments focus on machine learning algorithms to improve the obtained results with basic similarity measures. Support vector machines (SVM) shows that supervised learning procedure increases the performance of our content-based recommender systems; it yields best results in terms of AUC in comparison with other investigated methodologies such as Matrix Factorization and Collaborative Topic Regression.
Starting Page	1
Ending Page	17
Page Count	17
File Format	PDF
ISSN	18695450
Journal	Social Network Analysis and Mining
Volume Number	4
Issue Number	1
e-ISSN	18695469
Language	English
Publisher	Springer Vienna
Publisher Date	2014-08-21
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Job recommendation Facebook LinkedIn Content-based recommender system Social recommender systems SVM Data Mining and Knowledge Discovery Complex Networks Game Theory, Economics, Social and Behav. Sciences Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Methodology of the Social Sciences
Content Type	Text
Resource Type	Article
Subject	Information Systems Human-Computer Interaction Communication Computer Science Applications Media Technology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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