Techniques for comparing and recommending conferences

Abstract

This article defines, implements, and evaluates techniques to automatically compare and recommend conferences. The techniques for comparing conferences use familiar similarity measures and a new measure based on co-authorship communities, called co-authorship network community similarity index. The experiments reported in the article indicate that the technique based on the new measure performs better than the other techniques for comparing conferences, which is therefore the first contribution of the article. Then, the article focuses on three families of techniques for conference recommendation. The first family adopts collaborative filtering based on the conference similarity measures investigated in the first part of the article. The second family includes two techniques based on the idea of finding, for a given author, the strongest related authors in the co-authorship network and recommending the conferences that his co-authors usually publish in. The first member of this family is based on the Weighted Semantic Connectivity Score—WSCS, which is accurate but quite costly to compute for large co-authorship networks. The second member of this family is based on a new score, called the Modified Weighted Semantic Connectivity Score—MWSCS, which is much faster to compute and as accurate as the WSCS. The third family includes the Cluster-WSCS-based and the Cluster-MWSCS-based conference recommendation techniques, which adopt conference clusters generated using a subgraph of the co-authorship network. The experiments indicate as the best performing conference recommendation technique the Cluster-WSCS-based technique. This is the second contribution of the article. Finally, the article includes experiments that use data extracted from the DBLP repository and a web-based application that enables users to interactively analyze and compare a set of conferences.