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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-ofthe-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.

In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.