Academic Venue Recommendations Based on Similarity Learning of an Extended Nearby Citation Network

The rapidly increasing number of potential academic venues for research publication and commentary has made sourcing the venue that would best contribute to promoting effective scientific cooperation more challenging. In this paper, we propose a similarity learning approach to determine the most appropriate venue to publish an article. We first analyze the article metadata and cited articles to build the citation network matrices of the given article and then apply these to learn and build similarity matrices between academic objects (i.e., articles, authors, and venues) at an extended nearby article citation network. Using the formed matrices, we estimate a collaborative anticipation confidence score of a relationship between the venues in the extended network. For our empirical studies, we used an actual academic dataset to validate the efficiency of our approach and recommend an appropriate academic venue. The experimental results highlight the effectiveness of our proposed approach to optimize overall recommendation quality, compared with other baseline approaches.