Search

Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable recommendations; and model-based, whose recommendations are more precise but hard to understand. Here we propose a Bayesian model that not only provides us with recommendations as good as matrix factorization models, but these predictions can also be explained. The model is based on both user-based and item-based collaborative filtering approaches, which recommends items by using similar users' and items' information, respectively. Experiments carried out using four datasets present good results compared to several state-of-the-art baselines, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also improving the prediction's accuracy in some datasets.

Venezuelan public health by the end of nineteenth century was, to a large extent, based on ideas developed by President Antonio Guzmán Blanco, together with intellectuals and publicists, and first implemented through political and legal reforms during his rule from 1870-1877. A key aspect of Blanco’s administration on that regard was a controversy over Catholic cemeteries in northern Caracas, stirred up by the press and supported by contemporary hygienist theory, giving the state an excuse to shut them down. Guzmán's ideas showed European influences on his re-urbanization plans for the capital, which aimed, among other things, to gain control over cemeteries which until then had been under the Catholic Church’s control. This led to the construction of secular graveyards in the outskirts of the city.