Implementación de un modelo de aprendizaje automático para la recomendación de ítems.

Abstract

The present work of finalizing the presentation presents an implementation of a system of recommendation of items. The MovieLens data set that contains movies is used to refer to movies as items. Content information is extracted from the movies, for more features of the movies and recommendations. A series of pre-processing is applied to the content information of the films. Follow-up of the application of an automated learning technique within the probabilistic models called Dirichlet Latent Assignment (LDA). Storing the distribution of topics for each movie.