Learning Collaborative Information Filters
- Authors
- Daniel BillsusMichael J. Pazzani
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w4118752 →Countries where authors are citing Learning Collaborative Information Filters
This map shows the geographic impact of Learning Collaborative Information Filters. 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 Learning Collaborative Information Filters with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Learning Collaborative Information Filters more than expected).
Fields of papers citing Learning Collaborative Information Filters
This network shows the impact of Learning Collaborative Information Filters. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Learning Collaborative Information Filters.
About Learning Collaborative Information Filters
This paper, published in 1998, received 656 indexed citations . Written by Daniel Billsus and Michael J. Pazzani covering the research area of Management Science and Operations Research, Signal Processing and Information Systems. It is primarily cited by scholars working on Information Systems (557 citations), Computer Vision and Pattern Recognition (214 citations) and Artificial Intelligence (207 citations). Published in International Conference on Machine Learning.
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.
This paper is also available at doi.org/w4118752.