This dissertation discusses techniques to automatically analyze, compare and recommend conferences, using bibliographic data, outlines an implementation of the proposed techniques and describes experiments with data extracted from a triplified version of the DBLP repository. Conference analysis applies statistical and social network analysis measures to the co-authorship network. The techniques for comparing conferences explore familiar similarity measures, such as the Jaccard similarity coefficient, the Pearson correlation similarity and the cosine similarity, and a new measure, the co-authorship network communities similarity index. These similarity measures are used to create a conference recommendation system based on the Collaborative Filtering strategy. Finally, the work introduces two techniques for recommending conferences to a given prospective author based on the strategy of finding the most related authors in the co-authorship network. The first alternative uses the Katz index, which can be quite costly for large graphs, while the second one adopts an approximation of the Katz index, which proved to be much faster to compute. The experiments suggest that the best performing techniques are: the technique for comparing conferences that uses the new similarity measure based on co-authorship communities; and the conference recommendation technique that explores the most related authors in the co-authorship network.