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.
Learning and Revising User Profiles: The Identification of Interesting Web Sites
1997758 citationsMichael J. Pazzani, Daniel BillsusMachine Learningprofile →
Countries citing papers authored by Daniel Billsus
Since
Specialization
Citations
This map shows the geographic impact of Daniel Billsus'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 Daniel Billsus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Billsus more than expected).
This network shows the impact of papers produced by Daniel Billsus. 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 Daniel Billsus. The network helps show where Daniel Billsus may publish in the future.
Co-authorship network of co-authors of Daniel Billsus
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Billsus.
A scholar is included among the top collaborators of Daniel Billsus 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 Daniel Billsus. Daniel Billsus is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hilbert, David M., Daniel Billsus, & Laurent Denoue. (2006). Seamless Capture and Discovery for Corporate Memory.8 indexed citations
3.
Denoue, Laurent, David M. Hilbert, John Adcock, Daniel Billsus, & Matthew Cooper. (2005). ProjectorBox: Seamless presentation capture for classrooms. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2005(1). 1986–1991.10 indexed citations
4.
Hilbert, David M., Matthew Cooper, Laurent Denoue, John Adcock, & Daniel Billsus. (2005). <title>Seamless presentation capture, indexing, and management</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6015. 60150X–60150X.4 indexed citations
Webb, Geoffrey I., Michael J. Pazzani, & Daniel Billsus. (2001). Machine Learning for User Modeling. User Modeling and User-Adapted Interaction. 11(1-2). 19–29.219 indexed citations
10.
Billsus, Daniel & Michael J. Pazzani. (2000). User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction. 10(2-3). 147–180.304 indexed citations
Pazzani, Michael J. & Daniel Billsus. (1997). Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning. 27(3). 313–331.758 indexed citations breakdown →
19.
Pazzani, Michael J., Jack Muramatsu, & Daniel Billsus. (1996). Syskill & webert: Identifying interesting web sites. National Conference on Artificial Intelligence. 54–61.384 indexed citations
20.
Billsus, Daniel & Michael J. Pazzani. (1996). Revising User Profiles: The Search for Interesting Web Sites.12 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.