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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This map shows the geographic impact of Barry Smyth'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 Barry Smyth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Barry Smyth more than expected).
This network shows the impact of papers produced by Barry Smyth. 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 Barry Smyth. The network helps show where Barry Smyth may publish in the future.
Co-authorship network of co-authors of Barry Smyth
This figure shows the co-authorship network connecting the top 25 collaborators of Barry Smyth.
A scholar is included among the top collaborators of Barry Smyth 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 Barry Smyth. Barry Smyth is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
O’Mahony, Michael P. & Barry Smyth. (2010). A classification-based review recommender. Arrow@dit (Dublin Institute of Technology).6 indexed citations
7.
Smyth, Barry. (2009). The sensor web : bringing information to life. Arrow@dit (Dublin Institute of Technology). 3–3.1 indexed citations
8.
O’Donovan, John, et al.. (2006). Personalizing Trust in Online Auctions. eCite Digital Repository (University of Tasmania).4 indexed citations
9.
McCarthy, Kevin, María Salamó, Lorcan Coyle, et al.. (2006). CATS: A Synchronous Approach to Collaborative Group Recommendation. eCite Digital Repository (University of Tasmania). 86–91.77 indexed citations
10.
Lee, Hyowon, Alan F. Smeaton, & Barry Smyth. (2005). User evaluation outside the lab: the trial of Físchlár-News. Arrow@dit (Dublin Institute of Technology).1 indexed citations
11.
McCarthy, Kevin, Lorraine McGinty, Barry Smyth, & J.P. Reilly. (2005). On the evaluation of dynamic critiquing: a large-scale user study. National Conference on Artificial Intelligence. 535–540.9 indexed citations
12.
Dunne, Lucy E., Susan P. Ashdown, & Barry Smyth. (2005). Expanding garment functionality through embedded electronic technology. Journal of textile and apparel technology and management. 4(3).29 indexed citations
13.
O’Donovan, John & Barry Smyth. (2005). Eliciting Trust Values from Recommendation Errors.. The Florida AI Research Society. 289–294.9 indexed citations
14.
Coyle, Maurice & Barry Smyth. (2005). Explaining search results. International Joint Conference on Artificial Intelligence. 1553–1555.6 indexed citations
15.
Brady, Sarah, et al.. (2005). Wearable Sensors? What is there to sense?. PubMed. 117. 80–8.6 indexed citations
16.
Smyth, Barry, Evelyn Balfe, Oisín Boydell, et al.. (2005). A live-user evaluation of collaborative web search. International Joint Conference on Artificial Intelligence. 1419–1424.48 indexed citations
17.
Wilson, David C., et al.. (2003). Preserving Recommender Accuracy and Diversity in Sparse Datasets.. The Florida AI Research Society. 139–143.5 indexed citations
18.
McGinty, Lorraine & Barry Smyth. (2002). Shared Experiences in Personalized Route Planning. The Florida AI Research Society. 111–115.5 indexed citations
19.
Smyth, Barry. (2001). Improving recommendation diversity. National Conference on Artificial Intelligence.143 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.