Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Performance of recommender algorithms on top-n recommendation tasks
2010887 citationsPaolo Cremonesi, Roberto Turrin et al.Virtual Community of Pathological Anatomy (University of Castilla La Mancha)profile →
Sequence-Aware Recommender Systems
2018273 citationsMassimo Quadrana, Paolo Cremonesi et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Paolo Cremonesi
Since
Specialization
Citations
This map shows the geographic impact of Paolo Cremonesi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Paolo Cremonesi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paolo Cremonesi more than expected).
This network shows the impact of papers produced by Paolo Cremonesi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Paolo Cremonesi. The network helps show where Paolo Cremonesi may publish in the future.
Co-authorship network of co-authors of Paolo Cremonesi
This figure shows the co-authorship network connecting the top 25 collaborators of Paolo Cremonesi.
A scholar is included among the top collaborators of Paolo Cremonesi based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Paolo Cremonesi. Paolo Cremonesi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cremonesi, Paolo, et al.. (2021). On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text.. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 147–157.5 indexed citations
Deldjoo, Yashar, Markus Schedl, Paolo Cremonesi, & Gabriella Pasi. (2018). Content-based multimedia recommendation systems: Definition and application domains. BOA (University of Milano-Bicocca). 2140. 1–12.10 indexed citations
6.
Vall, Andreu, Massimo Quadrana, Markus Schedl, Gerhard Widmer, & Paolo Cremonesi. (2017). The Importance of Song Context in Music Playlists.. Conference on Recommender Systems.7 indexed citations
7.
Cremonesi, Paolo, et al.. (2017). Kernalized collaborative contextual bandits. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1905. 1–6.
8.
Deldjoo, Yashar, et al.. (2017). Enhancing Children’s Experience with Recommendation Systems. Virtual Community of Pathological Anatomy (University of Castilla La Mancha).10 indexed citations
9.
Vall, Andreu, Markus Schedl, Gerhard Widmer, Massimo Quadrana, & Paolo Cremonesi. (2017). The importance of song context in music playlists: Enabling recommendations in the long tail. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1905. 1–2.1 indexed citations
10.
Pagano, Roberto, et al.. (2016). Explicit Elimination of Similarity Blocking for Session-based Recommendation. Data Archiving and Networked Services (DANS). 1688. 1–2.1 indexed citations
11.
Hopfgartner, Frank, Andreas Lommatzsch, Benjamin Kille, et al.. (2016). The potentials of recommender systems challenges for student learning. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1–2.1 indexed citations
Paolini, Paolo, Paolo Cremonesi, & George Lekakos. (2013). Proceedings of the 11th European Conference on Interactive TV and Video.1 indexed citations
14.
Malucelli, Federico, et al.. (2012). An application of bicriterion shortest paths to collaborative filtering. Federated Conference on Computer Science and Information Systems. 423–429.2 indexed citations
Cremonesi, Paolo, Paolo Garza, Elisa Quintarelli, & Roberto Turrin. (2011). Top-N recommendations on Unpopular Items with Contextual Knowledge. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–5.10 indexed citations
17.
Cremonesi, Paolo, et al.. (2011). Hybrid algorithms for recommending new items in personal TV. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 720. 1–6.2 indexed citations
18.
Cremonesi, Paolo, et al.. (2011). On the cooling-aware workload placement problem. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 2–7.3 indexed citations
19.
Cremonesi, Paolo & Marco Bertoli. (2009). Predicting SPEC Benchmarks Values for Untested Systems.. Virtual Community of Pathological Anatomy (University of Castilla La Mancha).1 indexed citations
20.
Cremonesi, Paolo & Giuliano Casale. (2007). How to select significant workloads in performance models. 183–192.6 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.