The automation of learning object recommendation and learning styles detection processes has attracted the interest of many researchers. Some works consider learning styles to recommend learning objects. On the other hand, other works automatically detect learning styles, analyzing the behavior of students in intelligent tutorial systems in relation to the use of learning objects. Taking into account that advances in this field of research have been constantly presented in recent years, this paper analyzes the results of works discovered through a systematic literature review. The main objective was to discover and document the relationships between learning styles and learning objects considered by researchers in order to provide accurate content recommendations. The results show inconsistencies in the process, indicating that more and more in-depth research is still needed to allow a more accurate understanding of how to consider learning styles in the learning object recommendation process.