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ICML
1998
IEEE
15 years 10 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
15 years 10 months ago
Assessing data mining results via swap randomization
The problem of assessing the significance of data mining results on high-dimensional 0?1 data sets has been studied extensively in the literature. For problems such as mining freq...
Aristides Gionis, Heikki Mannila, Panayiotis Tsapa...
ECIR
2006
Springer
14 years 11 months ago
Clustering-Based Searching and Navigation in an Online News Source
The growing amount of online news posted on the WWW demands new algorithms that support topic detection, search, and navigation of news documents. This work presents an algorithm f...
Simón C. Smith, M. Andrea Rodríguez
PVLDB
2011
14 years 5 months ago
Hyper-local, directions-based ranking of places
Studies find that at least 20% of web queries have local intent; and the fraction of queries with local intent that originate from mobile properties may be twice as high. The eme...
Petros Venetis, Hector Gonzalez, Christian S. Jens...
TKDE
2008
115views more  TKDE 2008»
14 years 10 months ago
Time-Aware Web Users' Clustering
Web users clustering is a crucial task for mining information related to users needs and preferences. Up to now, popular clustering approaches build clusters based on usage pattern...
Sophia G. Petridou, Vassiliki A. Koutsonikola, Ath...