A comprehensive view of recommendation methods based on probabilistic techniques [Una Comprensiva Revisión de los Métodos de Recomendación basados en Técnicas Probabilísticas]

Abstract

This research aims to use a hybrid recommendation method based on probabilistic techniques and topics modeling that provide recommendations most close fitting the user compared to other traditional recommendation models. We carry out a comprehensive review of the recommended methods for content-based systems and collaborative filtering, mainly in the domain of recommending movies. The methods discussed are the matrix factorization and Latent Dirichlet Allocation method. The literature review around these models focuses on identifying problems and open issues that may be covered for future researches. Also, we analyzed the recommendation models that integrant latent factor methods and topics modeling, which will be used to compare results obtained with the hybrid model.