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» Search Engines that Learn from Implicit Feedback
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107
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SIGIR
2009
ACM
15 years 5 months ago
On social networks and collaborative recommendation
Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided an...
Ioannis Konstas, Vassilios Stathopoulos, Joemon M....
101
Voted
MM
2006
ACM
158views Multimedia» more  MM 2006»
15 years 5 months ago
Extreme video retrieval: joint maximization of human and computer performance
We present an efficient system for video search that maximizes the use of human bandwidth, while at the same time exploiting the machine’s ability to learn in real-time from use...
Alexander G. Hauptmann, Wei-Hao Lin, Rong Yan, Jun...
KDD
2006
ACM
118views Data Mining» more  KDD 2006»
15 years 11 months ago
Reducing the human overhead in text categorization
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
Arnd Christian König, Eric Brill
102
Voted
FDG
2009
ACM
15 years 5 months ago
Emphasizing soft skills and team development in an educational digital game design course
Engineering education has evolved from providing students solely with technical skills to providing them with courses that provide students with the non-technical “soft skills...
Quincy Brown, Frank J. Lee, Suzanne Alejandre
80
Voted
WWW
2004
ACM
15 years 11 months ago
Web-scale information extraction in knowitall: (preliminary results)
Manually querying search engines in order to accumulate a large body of factual information is a tedious, error-prone process of piecemeal search. Search engines retrieve and rank...
Oren Etzioni, Michael J. Cafarella, Doug Downey, S...