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CIKM
2010
Springer
15 years 4 months ago
Learning to rank relevant and novel documents through user feedback
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Abhimanyu Lad, Yiming Yang
WWW
2011
ACM
15 years 1 months ago
Learning to rank with multiple objective functions
We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...
PVLDB
2011
15 years 1 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...
KDD
2008
ACM
161views Data Mining» more  KDD 2008»
16 years 6 months ago
Locality sensitive hash functions based on concomitant rank order statistics
: Locality Sensitive Hash functions are invaluable tools for approximate near neighbor problems in high dimensional spaces. In this work, we are focused on LSH schemes where the si...
Kave Eshghi, Shyamsundar Rajaram
CORR
2010
Springer
165views Education» more  CORR 2010»
15 years 4 months ago
Monte Carlo Methods for Top-k Personalized PageRank Lists and Name Disambiguation
We study a problem of quick detection of top-k Personalized PageRank lists. This problem has a number of important applications such as finding local cuts in large graphs, estima...
Konstantin Avrachenkov, Nelly Litvak, Danil Nemiro...