Novel and Classic Metaheuristics for Tunning a Recommender System for Predicting Student Performance in Online Campus

Mathematics

• Recommender Systems 100%
• Metaheuristics 74%
• Direct Search 59%
• Performance 35%
• Collaborative Filtering 34%
• Parameter Learning 32%
• Prediction 32%
• Soft Computing 29%
• Learning Rate 27%
• Learning Process 27%
• Stochastic Gradient 26%
• Matrix Factorization 25%
• Gradient Descent 24%
• Evolutionary Algorithms 23%
• Prediction Error 23%
• Predict 18%
• Two Parameters 17%
• Regularization 17%
• Optimization Problem 14%
• Unknown 13%
• Computing 12%
• Context 12%
• Term 10%

Engineering & Materials Science

• Recommender systems 60%
• Students 44%
• Soft computing 23%
• Collaborative filtering 22%
• Factorization 19%
• Evolutionary algorithms 18%