Analyzing MOOC Entries of Professionalson LinkedIn for User Modeling andPersonalized MOOC RecommendationsGuangyuan Piao, John G. BreslinInsight Centre for Data Analytics, National University of Ireland, Galway

Centre forData 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’ profilesfrom LinkedIn allows to produce useful user profiles which can be used forpersonalized 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

performanceofrecommenda,ons

approa

ches

skill-based job-based edu-based pop

Figure 5. The quality of recommendations using different user modelingstrategies. 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