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.
Large Language Models can Accurately Predict Searcher Preferences
202441 citationsPaul Thomas, Seth Spielman et al.profile →
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 Paul Thomas'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 Paul Thomas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul Thomas more than expected).
This network shows the impact of papers produced by Paul Thomas. 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 Paul Thomas. The network helps show where Paul Thomas may publish in the future.
Co-authorship network of co-authors of Paul Thomas
This figure shows the co-authorship network connecting the top 25 collaborators of Paul Thomas.
A scholar is included among the top collaborators of Paul Thomas 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 Paul Thomas. Paul Thomas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Thomas, Paul & Virendra Singh. (2024). Study of Narcissism through Instagram Influencers. International Journal of Innovative Science and Research Technology (IJISRT). 2473–2477.1 indexed citations
Trippas, Johanne R. & Paul Thomas. (2019). Data sets for spoken conversational search. RMIT Research Repository (RMIT University Library). 2337. 14–18.2 indexed citations
9.
Scholer, Falk, Paul Thomas, & Alistair Moffat. (2013). Observing users to validate models. RMIT Research Repository (RMIT University Library). 11–13.1 indexed citations
10.
Milne, David, Paul Thomas, & Cécile Paris. (2012). Finding, Weighting and Describing Venues: CSIRO at the 2012 TREC Contextual Suggestion Track. Text REtrieval Conference.5 indexed citations
11.
Karimi, Sarvnaz, Jie Yin, & Paul Thomas. (2012). Searching and Filtering Tweets: CSIRO at the TREC 2012 Microblog Track. Text REtrieval Conference.4 indexed citations
Hawking, David, et al.. (2009). C-TEST: Supporting novelty and diversity in testfiles for search tuning. ANU Open Research (Australian National University).3 indexed citations
16.
Balog, Krisztian, Paul Thomas, Nick Craswell, et al.. (2008). Overview of the TREC 2008 Enterprise Track. Text REtrieval Conference.51 indexed citations
17.
Bailey, Peter, Paul Thomas, & David Hawking. (2007). Does brandname influence perceived search result quality? Yahoo!. ANU Open Research (Australian National University).10 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.