A Paper Recommendation System with ReaderBench: The Graphical Visualization of Semantically Related Papers and Concepts

Ionut Cristian Paraschiv (1) , Mihai Dascalu (1) , Philippe Dessus (2) , Stefan Trausan-Matu (1) , Danielle S. Mcnamara (3)

1 Université Politehnica
2 LaRAC - Laboratoire de Recherche sur les Apprentissages en Contexte
3 ASU - Arizona State University [Tempe]

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.

Mots clés: paper recommendation system; scientometrics; semantic similarity; discourse analysis

Domaines: Education

Ionut Cristian Paraschiv, Mihai Dascalu, Philippe Dessus, Stefan Trausan-Matu, Danielle S. Mcnamara. A Paper Recommendation System with ReaderBench: The Graphical Visualization of Semantically Related Papers and Concepts. Y. Li; M. Chang; M. Kravcik; E. Popescu; R. Huang; Kinshuk; N.-S. Chen. State-of-the-art and future directions of smart learning, LNET Series, Springer, pp.443-449, 2016, 978-981-287-868-7. ⟨hal-01217025⟩