Multi-objective recommender system for corporate MOOC.

Mandarine Academy is a major MOOC (Massive Open Online Course) operator with more than 100 active e-learning platforms in multiple languages and 550K overall users. The company is providing a wide range of content types to help users in their learning process. With thousands of pedagogical online resources in an everyday growing catalog, users can have a hard time finding relevant content. Mandarine Academy is looking to improve the overall user experience while minimizing both information overload and drop rate levels. This paper details the conception and implementation of a multi-objective e-learning recommender system. The proposed approach takes advantage of multiple known implementations like content-based and collaborative filtering among other techniques to generate an initial population of solutions. A custom genetic operator is proposed and compared to classical operators using multiple algorithms (NSGA-II, NSGA-III, SPEA-2, IBEA, and MOEA/D). Using the best configurations found by the tuning process, we showcase the initial results obtained by each approach when applied to real-world data from a public MOOC provided by the company.