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ARPPA: Mining Professional Profiles from LinkedIn Using Association Rules

Authors:
Paula Silva
Wladmir Brandão

Keywords: Data-mining, association rules, LinkedIn

Abstract:
Human resources managers design and develop professional profiles to maximize the organization workforce. These organizations typically maintain extensive static resume databases from where managers extract and analyze professional data, discovering people with appropriate knowledge, skills and experience to fulfill organizational positions. Nowadays, online professional networks, such as LinkedIn, provide a rich, dynamic, and massive scale resume database useful for professional profile analyses. Considering such massive scale databases, while manual analysis is an exhaustive and often prohibitive task, the use of data mining techniques allows managers to effectively the huge volume of data with a lower cost. In particular, for educational institutions focused on the development of persons with knowledge and skills required by organizations, the use of data mining techniques over professional networks is paramount to plan, direct and implement academic activities and curricula. In this article, we introduce ARPPA, a novel approach to discover professional profile patterns from LinkedIn by using association rules mining. Particularly, our approach crawls resumes from LinkedIn uses a multidimensional data model suitable for professional profile analyses to create and load the crawled data to a data warehouse, and extracts relevant patterns from the data warehouse using an Apriori algorithm. Additionally, we evaluate our approach attesting its usefulness to plan, direct and implement academic activities and curricula in educational institutions.

Pages: 72 to 77

Copyright: Copyright (c) IARIA, 2015

Publication date: February 22, 2015

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-61208-386-5

Location: Lisbon, Portugal

Dates: from February 22, 2015 to February 27, 2015