This study presents a comprehensive taxonomy of techniques used in academic recommendation systems, presenting a detailed classification and quantification of methods in different academic fields. Through the analysis of key areas such as literature, collaborators, authors, conferences, journals, data sets, and grants, we identify six major technical categories: content-based filtering (CBF), collaborative filtering (CF), hybrid systems, graph-based methods, statistical and mathematical models, information retrieval (IR), and optimization-based methods. Content-Based Filtering (CBF) and hybrid systems appear to be the most common techniques, especially in literature and journal recommendations. Analysis reveals different trends in the application of technology, with Collaborative Filtering (CF) especially prevalent in conference recommendations and Information Retrieval (IR) and optimization-based methods primarily used in reviewer recommendations. The findings provide valuable insight into the current landscape and the evolution of scientific recommendation systems and highlight the importance of adaptable and multifaceted approaches to meet the specific needs of different academic areas. The aim of this study is to guide future research and development in the creation of more effective and domain-specific recommendation systems for the academic community.