An Intelligent Approach to Design and Development of Personalized Meta Search: Recommendation of Scientific Articles

Abstract

In this article we present a method to recommend articles scientists taking into account their degree of generality or specificity. In terms of methodology, two approaches are presented to recommend articles based on Topic Modeling. The first of these is based on the divergence of topics that are given in the documents, while the second is based on the similarity between these topics. After a validation process it was demonstrated that the proposed methods are more efficient than the traditional methods.

Findings Seminal Papers Using Data Mining Techniques

“…The importance of identifying the so-called seminal articles has been recognized as a de facto standard in the realization of a state of the art in the most dissimilar disciplines. To identify these articles of unquestionable significance in an investigation (Berkani, Hanifi, & Dahmani, 2020;Silva, Villa, & Cabrera, 2020), different alternatives have been proposed such as the use of collaborative models (Wang & Blei, 2011) and the use of personalized systems for the recommendation of the most relevant articles (Pera & Ng, 2011). Less studied has been the fact of how to identify these and their possible genealogy (Bae, Hwang, Kim, & Faloutsos, 2011, 2014.…”