Literature Review on Information Filtering Methods in Recommendation Systems
Cupertino Lucero-Álvarez, Perfecto M. Quintero-Flores, Pascual Perez-Cruz, Carlos A. Ortíz-Ramírez, Patricia Mendoza-Crisóstomo, Juventino Montiel-Hernández · 2021
This article presents a review of the literature on the information filtering methods used in current Recommender Systems, with an emphasis on recommendation Systems based on content, and those based on collaboration. The study starts from the classic taxonomy of filtering methods: content-based and collaboration-based, their mechanisms are explored, advantages and areas of opportunity are cited, and knowledge-based filtering is also incorporated. It concludes with an easy-to-understand literary review that seeks to provide the reader with an overview of the classic mechanisms behind Recommendation Systems, and some of the techniques widely used in the models. The study of modern techniques that could be used to improve the recommendations in the different types of filtering, such as those that use mixed approaches and those that use knowledge graphs, is left in perspective.