Discrete mixed membership modeling and continuous latent factor modeling (also known as matrix factorization) are two popular, complementary approaches to dyadic data analysis. In...
Recommender Systems, based on collaborative filtering (CF), aim to accurately predict user tastes, by minimising the mean error achieved on hidden test sets of user ratings, afte...
This work introduces a new query inference model that can access data and communicate with a teacher by asking finitely many boolean queries in a language L. In this model the pa...
Traditional Web-based educational systems still have several shortcomings when comparing with a real-life classroom teaching, such as lack of contextual and adaptive support, lack...
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative fil...