Paraskevi Fragopoulou

Prof. Dr. Paraskevi Fragopoulou

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Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 January 2022 in Knowledge and Information Systems
Verified
Journal Article
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HPHarris Papadakis
Harris Papadakis
APAntonis Papagrigoriou
Antonis Papagrigoriou
Costas PanagiotakisCostas Panagiotakis
Costas Panagiotakis
EKEleftherios Kosmas
Eleftherios Kosmas
PFParaskevi Fragopoulou
Paraskevi Fragopoulou

1 November 2021 in Expert Systems with Applications
Verified
Journal Article
Closed access
Costas PanagiotakisCostas Panagiotakis
Costas Panagiotakis
HPHarris Papadakis
Harris Papadakis
APAntonis Papagrigoriou
Antonis Papagrigoriou
PFParaskevi Fragopoulou
Paraskevi Fragopoulou

30 April 2020 in International Journal of Machine Learning and Cybernetics
Verified
Journal Article
Closed access
PDF file
Costas PanagiotakisCostas Panagiotakis
Costas Panagiotakis
HPHarris Papadakis
Harris Papadakis
PFParaskevi Fragopoulou
Paraskevi Fragopoulou

13 July 2016 in Social Network Analysis and Mining
Verified
Journal Article
Closed access
PDF file
DADespoina Antonakaki
Despoina Antonakaki
IPIasonas Polakis
Iasonas Polakis
EAElias Athanasopoulos
Elias Athanasopoulos
Sotiris IoannidisSotiris Ioannidis
Sotiris Ioannidis
PFParaskevi Fragopoulou
Paraskevi Fragopoulou

1 June 2023 in Foundations of Computing and Decision Sciences
Verified
Journal Article
Open access
PDF file
HPHarris Papadakis
Harris Papadakis
APAntonis Papagrigoriou
Antonis Papagrigoriou
EKEleftherios Kosmas
Eleftherios Kosmas
Costas PanagiotakisCostas Panagiotakis
Costas Panagiotakis
SMSmaragda Markaki
Smaragda Markaki
PFParaskevi Fragopoulou
Paraskevi Fragopoulou
In the era of internet access, recommender systems try to alleviate the difficulty consumers face while trying to find items (e.g. services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of content-based recommender systems, and not only in one part of it. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of content-based recommender systems according to their ability to efficiently handle well-known drawbacks.

16 February 2018 in Social Network Analysis and Mining
Verified
Journal Article
Closed access
PDF file
DADespoina Antonakaki
Despoina Antonakaki
Sotiris IoannidisSotiris Ioannidis
Sotiris Ioannidis
PFParaskevi Fragopoulou
Paraskevi Fragopoulou