In recommender systems, machine learning models are used to predict the rating rᵤᵢ of a user u on an item i. At inference time, we recommend to each user u the items l having highest p… 展开

Recommender system can be classified according to the kind of information used to predict user preferences as Content-Based or Collaborative Filtering. 展开

Matrix factorization algorithms are probably the most popular and effective collaborative filtering methods for recommender systems. Matrix factorization is a latent factor model ass… 展开

Collaborative filtering methods based on matrix factorization often produce excellent results, but in cold-start scenarios—where little to no interaction data is available for new items and u… 展开