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Recommender System

Recommender System

Each learner is different. Not only in their background and cognitive capacities, but as well in their preferences and motivations across different topics and domains.

Moreover, the level of motivation and engagement can change over time for each individual.

Adaptive Learning protocols can be at their full scale of efficiency only when a Recommender System is in place. At EdCortex, one of our priorities is to help the EdTech enterprises to adopt a recommender system based on their needs and capabilities.

Example of algorithms: Multimodal Embedding and classification, Approximate Nearest Neighbors, Bayesian Personalized Ranking, Matrix Factorization

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