分析专业人士在LinkedIn上的MOOC条目，为用户建模和个性化的MOOC建议

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI:10.1145/2930238.2930264

Guangyuan Piao, J. Breslin

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引用次数: 17

摘要

这项工作的主要贡献是比较了三种基于LinkedIn个人资料中职位、教育领域和技能的用户建模策略，以便在冷启动情况下进行个性化MOOC推荐。结果表明，基于技能的用户建模策略效果最好，其次是基于工作和教育的用户建模策略。

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

The main contribution of this work is the comparison of three user modeling strategies based on job titles, educational fields and skills in LinkedIn profiles, 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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Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

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