Recommender System

Every learner is unique, not only in terms of their background and cognitive abilities, but also in their preferences and motivations in various subjects and fields.

Furthermore, an individual's level of motivation and engagement can fluctuate over time.

For Adaptive Learning protocols to be most effective, a Recommender System is essential. At EdCortex, we prioritize assisting EdTech companies in implementing a recommender system tailored to their specific requirements and capabilities.

Some examples of our algorithms include Multimodal Embedding and Classification, Approximate Nearest Neighbors, Bayesian Personalized Ranking, and Matrix Factorization.