Collaborative Filtering For Search
Collaborative Filtering for Search is a personalized search method that leverages user interaction data to predict relevance by identifying patterns and similarities among users' search behaviors, distinguishing itself from other search personalization techniques through its reliance on collective behavioral signals rather than explicit content analysis or individual profile data.
84
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8.4K
Citations
237
Authors
101
Institutions
Publications per year
2017–2026
20
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84
An Algorithmic Framework for Performing Collaborative Filtering
Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers et al. · ACM SIGIR Forum · 2017 · 2.6K citations
An algorithmic framework for performing collaborative filtering
Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers et al. · 1999 · 2K citations · Full text
Al Mamunur Rashid, István Albert, Dan Cosley et al. · 2002 · 537 citations
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101

University of Minnesota
Minneapolis, United States
4 h-index
22 publications
22.4K citations
University of Washington
Seattle, United States
2 h-index
4 publications
98 citations
University of California San Diego
San Diego, United States
2 h-index
4 publications
96 citations
University of California, Santa Cruz
Santa Cruz, United States
2 h-index
4 publications
324 citations
Indian Institute of Technology Kharagpur
Kharagpur, India
2 h-index
2 publications
23 citations

Stockholm University
Stockholm, Sweden
2 h-index
2 publications
59 citations

Boeing (United States)
Chicago, United States
2 h-index
2 publications
49 citations

Hanyang University
Seoul, South Korea
2 h-index
6 publications
120 citations
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2 publications · 79 citations
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FGCS · 2 publications · 67 citations
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2 publications · 28 citations
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Conference · 2 publications · 23 citations
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ISJ · 1 publications · 30 citations
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