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Analyzing MOOC Entries of Professionals
on LinkedIn for User Modeling and
Personalized MOOC Recommendations
Guangyuan Piao...
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UMAP2016EA - Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations

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The main contribution of this work is the comparison of three user modeling strategies based on job titles, educational fields and skills in LinkedIn pro les, for personalized MOOC recommendations in a cold start situation. Results show that the skill-based user modeling strategy performs best, followed by the job- and edu-based strategies.

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UMAP2016EA - Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations

  1. 1. 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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