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Fedelucio Narducci
Researcher at Instituto Politécnico Nacional
Publications - 102
Citations - 1466
Fedelucio Narducci is an academic researcher from Instituto Politécnico Nacional. The author has contributed to research in topics: Recommender system & Computer science. The author has an hindex of 17, co-authored 83 publications receiving 1092 citations. Previous affiliations of Fedelucio Narducci include University of Bari & University of Milano-Bicocca.
Papers
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Semantics-Aware Content-Based Recommender Systems.
TL;DR: This chapter presents a comprehensive survey of semantic representations of items and user profiles that attempt to overcome the main problems of the simpler approaches based on keywords and proposes a classification of semantic approaches into top-down and bottom-up.
Book ChapterDOI
Semantics-Aware Content-Based Recommender Systems
TL;DR: A comprehensive survey of semantic representations of items and user profiles can be found in this paper, where the authors propose a classification of semantic approaches into top-down and bottom-up.
Proceedings ArticleDOI
ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud
TL;DR: A framework which exploits the information available in the Linked Open Data cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm and the preliminary results provided us with encouraging findings.
Journal ArticleDOI
Linked open data-based explanations for transparent recommender systems
TL;DR: A framework that generates natural language explanations supporting the suggestions generated by a recommendation algorithm that is both algorithm-independent and domain-independent, and can be used to explain a single recommendation as well as a group of recommendations.
Book ChapterDOI
Learning Preference Models in Recommender Systems
Marco de Gemmis,Leo Iaquinta,Pasquale Lops,Cataldo Musto,Fedelucio Narducci,Giovanni Semeraro +5 more
TL;DR: The issue of learning preference models is dealt with, the most popular techniques for profile learning and preference elicitation are shown, and methods for feedback gathering in recommender systems are analyzed.