Paper Recommendation System: A Global and Soft Approach

Abstract : Paper recommendation to researchers has been extensively studied in the last years, and many methods have been investigated for this purpose. In this paper, we propose a novel approach embedding the whole process for selecting papers of interest given some keywords. Our approach is based on a workflow integrating fuzzy clustering of the papers, the computation of a representative summary paper per cluster using OWA operators, and ranking, in order to answer user queries adequately. The originality of our method relies in the introduction of fuzziness for more flexibility in the approach. The use of representative papers allows us to summarize sets of papers into a single representative one, thus simplifying the users interactions with the huge number of papers from the literature.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00803915
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Siwipa Pruitikanee, Lisa Di Jorio, Anne Laurent, Michel Sala. Paper Recommendation System: A Global and Soft Approach. FUTURE COMPUTING, Jun 2012, Nice, France. ⟨lirmm-00803915⟩

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