A Paper Recommendation System with ReaderBench: The Graphical Visualization of Semantically Related Papers and Concepts
The task of tagging papers with semantic metadata in order to analyze their relatedness represents a good foundation for a paper recommender system. The analysis from this paper extends from previous research in order to create a graph of papers from a specific domain with the purpose of determining each article’s importance within the considered corpus of papers. Moreover, as non-latent representations are powerful when used in conjunction with latent ones, our system retrieves semantically close words, not present in the paper, in order to improve the retrieval of papers. Our previous analyses used the semantic representation of papers in different semantic models with the purpose of creating visual graphs based on the semantic relatedness links between the abstracts. The current analysis takes a step forward by proposing a model that can suggest which papers are of the highest relevance, share similar concepts, and are semantically related with the initial query. Our study is performed using paper abstracts in the field of information technology extracted from the Web of Science citation index. The research includes a use case and its corresponding results by using interactive and exploratory network graph representations.
KeywordsPaper recommendation system Scientometrics Semantic similarity Discourse analysis
The work presented in this paper was partially funded by the FP7 2008-212578 LTfLL project, by the Sectorial Operational Programme Human Resources Development 2007–2013 of the Ministry of European Funds through the Financial Agreement POSDRU/159/1.5/S/134398, as well as by the NSF grants 1417997 and 1418378 to Arizona State University. We also thank Pablo Jensen and Sebastian Grauwin for providing the initial corpus of paper abstracts, and we are grateful to Cecile Perret for her help in preparing this paper.
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