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.
Trust in recommender systems
2005563 citationsJohn O’Donovan, Barry Smythprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by John O’Donovan
Since
Specialization
Citations
This map shows the geographic impact of John O’Donovan'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 John O’Donovan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John O’Donovan more than expected).
This network shows the impact of papers produced by John O’Donovan. 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 John O’Donovan. The network helps show where John O’Donovan may publish in the future.
Co-authorship network of co-authors of John O’Donovan
This figure shows the co-authorship network connecting the top 25 collaborators of John O’Donovan.
A scholar is included among the top collaborators of John O’Donovan 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 John O’Donovan. John O’Donovan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Schaffer, James, Tobias Höllerer, & John O’Donovan. (2015). Hypothetical Recommendation: A Study of Interactive Profile Manipulation Behavior for Recommender Systems. The Florida AI Research Society. 507–512.16 indexed citations
9.
Tintarev, Nava, et al.. (2015). Inspection Mechanisms for Community-based Content Discovery in Microblogs.. Conference on Recommender Systems. 21–28.6 indexed citations
10.
Adalı, Sibel, Md Tanvir Al Amin, Tarek Abdelzaher, et al.. (2014). Finding true and credible information on Twitter. International Conference on Information Fusion. 1–8.12 indexed citations
O’Donovan, John, et al.. (2008). PeerChooser. 1085–1088.120 indexed citations
14.
O’Donovan, John, et al.. (2007). Extracting and visualizing trust relationships from online auction feedback comments. International Joint Conference on Artificial Intelligence. 2826–2831.28 indexed citations
O’Donovan, John, et al.. (2006). Personalizing Trust in Online Auctions. eCite Digital Repository (University of Tasmania).4 indexed citations
17.
O’Donovan, John & Barry Smyth. (2005). Trust no one: evaluating trust-based filtering for recommenders. International Joint Conference on Artificial Intelligence. 1663–1665.15 indexed citations
18.
O’Donovan, John & Barry Smyth. (2005). Eliciting Trust Values from Recommendation Errors.. The Florida AI Research Society. 289–294.9 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.