Daniel Billsus

7.5k citations
24 papers · 5.8k · 3 hit papers · h-index 16

Impact in

Papers in

Daniel Billsus

23 papers receiving 5.3k citations

Daniel Billsus's Hit Papers

Content-Based Recommendation Systems 2007 · 1.9k citations
1.9k0+9+19Years since publication50010001.5k

Peers

Daniel Billsus
Comparison fields: 5 of 129
  • Information Systems 4.6k
  • Computer Vision and Pattern Recognition 1.6k
  • Artificial Intelligence 2.6k
  • Signal Processing 674
  • Computer Science Applications 333
Replace J. Ben Schafer with:
J. Ben Schafer United States
Alexander Felfernig Austria
Brent Smith United States
Jesús Bobadilla Spain
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Shaoping Ma China
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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).

Fields of papers citing papers by Daniel Billsus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 20 scholars most cited alongside Daniel Billsus, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Billsus Line = papers co-authored together Daniel Billsus links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Content-Based Recommendation Systems
Hit paper breakdown →
20071878
2
Learning and Revising User Profiles: The Identification of Interesting Web Sites
Hit paper breakdown →
1997972
3
Learning Collaborative Information Filters
Hit paper breakdown →
1998812
4
Syskill & webert: Identifying interesting web sites
1996475
5 2000379
6 2001297
7 1999283
8 2002204
9 1999177
10 200089
11 200757
12 200549
13 200239
14 199830
15 199729
16 199915
17
Revising User Profiles: The Search for Interesting Web Sites
199613
18
ProjectorBox: Seamless presentation capture for classrooms
200510
19
Seamless Capture and Discovery for Corporate Memory
20068
20 20046

About Daniel Billsus

Daniel Billsus is a scholar working on Information Systems, Artificial Intelligence, Signal Processing, Computer Networks and Communications and Sociology and Political Science, having authored 24 papers that have together received 5.8k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (11 papers), Web Data Mining and Analysis (6 papers), Data Management and Algorithms (5 papers), Data Mining Algorithms and Applications (4 papers), Topic Modeling (4 papers), Multimedia Communication and Technology (3 papers), Machine Learning and Algorithms (3 papers) and Peer-to-Peer Network Technologies (3 papers). The work is most often cited by research in Information Systems (4.6k citations), Computer Vision and Pattern Recognition (1.6k citations), Artificial Intelligence (2.6k citations), Signal Processing (674 citations) and Computer Science Applications (333 citations). Daniel Billsus has collaborated with scholars based in United States, Russia and Australia. Frequent co-authors include Michael J. Pazzani, Jack Muramatsu, Geoffrey I. Webb, Clifford Brunk, James Chen, David M. Hilbert, Dan Maynes-Aminzade, Laurent Denoue, John Adcock and Matthew Cooper. Their work appears in journals such as User Modeling and User-Adapted Interaction, Autonomous Agents and Multi-Agent Systems, Journal of the American Podiatric Medical Association, Communications of the ACM and AI Magazine.

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.

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