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Analyzing MOOC Entries of Professionals
on LinkedIn for User Modeling and
Personalized MOOC Recommendations
Guangyuan Piao, John G. Breslin
Insight Centre for Data Analytics, National University of Ireland, Galway
Centre for
Data Analytics
Introduction
Massive Open Online Courses (MOOCs) play a significant role in educat-
ing professionals. According to a recent study, over half of MOOC learners
(62.4%) reported themselves as being employed full-time or self-employed.
Figure 1. LinkedIn functionality of adding finished MOOCs to user profiles.
Aim of Work
To investigate whether information in different fields of professionals’ profiles
from LinkedIn allows to produce useful user profiles which can be used for
personalized MOOC recommendations.
Three main fields of LinkedIn profile
• job titles: Software Engineer, Java Engineer
• education fields: Information Engineering
• skills: Java, C++, Microsoft Excel
User Modeling Strategies
Figure 2. Skill-based user profiles.
Figure 3. Job-based user profiles.
Figure 4. Edu-based user profiles.
Experiment Setup
MOOC recommendations
• dataset: 4,401 LinkedIn profiles (321 users for test set)
• task: recommending MOOCs based on user profiles
• recommendation algorithm:
sim (u, c) =
Pu
||Pu||
· Pi (1)
Evaluation Metrics
• MRR: Mean Reciprocal Rank
• S@N: Success rate at rank N
Results
0.19	
0.23	
0.21	
0.29	
0.24	
0.33	
0.26	
0.35	
0	 0.05	 0.1	 0.15	 0.2	 0.25	 0.3	 0.35	 0.4	
MRR	
S@05	
performance	of	recommenda,ons	
approaches	
skill-based	 job-based	 edu-based	 pop	
Figure 5. The quality of recommendations using different user modeling
strategies. pop denotes a non-personalized recommender which recommends
the most popular MOOCs among learners.
The Insight Centre for Data Analytics is supported by Science Foundation Ireland under Grant Number SFI/12/RC/2289

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