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.
Content-boosted collaborative filtering for improved recommendations
2002693 citationsPrem Melville, Ramadass Nagarajan et al.National Conference on Artificial Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Prem Melville'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 Prem Melville with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prem Melville more than expected).
This network shows the impact of papers produced by Prem Melville. 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 Prem Melville. The network helps show where Prem Melville may publish in the future.
Co-authorship network of co-authors of Prem Melville
This figure shows the co-authorship network connecting the top 25 collaborators of Prem Melville.
A scholar is included among the top collaborators of Prem Melville 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 Prem Melville. Prem Melville is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Melville, Prem, Jure Leskovec, & Foster Provost. (2010). Proceedings of the First Workshop on Social Media Analytics. Knowledge Discovery and Data Mining.13 indexed citations
7.
Attenberg, Josh, Prem Melville, & Foster Provost. (2010). Guided Feature Labeling for Budget-Sensitive Learning Under Extreme Class Imbalance.3 indexed citations
8.
Niculescu-Mizil, Alexandru, Claudia Perlich, Grzegorz Świrszcz, et al.. (2009). Winning the KDD Cup Orange Challenge with ensemble selection. Knowledge Discovery and Data Mining. 23–34.39 indexed citations
Melville, Prem & Raymond J. Mooney. (2003). Constructing diverse classifier ensembles using artificial training examples. International Joint Conference on Artificial Intelligence. 505–510.172 indexed citations
17.
Melville, Prem. (2003). Creating Diverse Ensemble Classifiers.11 indexed citations
18.
Melville, Prem, et al.. (2002). Content-boosted collaborative filtering for improved recommendations. National Conference on Artificial Intelligence. 187–192.693 indexed citations breakdown →
Mooney, Raymond J., Prem Melville, Lappoon R. Tang, et al.. (2002). Relational Data Mining with Inductive Logic Programming for Link Discovery.34 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.