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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
11 years 10 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
14 years 8 months ago
Query chains: learning to rank from implicit feedback
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
Filip Radlinski, Thorsten Joachims
CIKM
2009
Springer
14 years 2 months ago
Learning to rank graphs for online similar graph search
Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chem...
Bingjun Sun, Prasenjit Mitra, C. Lee Giles
SDM
2007
SIAM
169views Data Mining» more  SDM 2007»
13 years 9 months ago
Rank Aggregation for Similar Items
The problem of combining the ranked preferences of many experts is an old and surprisingly deep problem that has gained renewed importance in many machine learning, data mining, a...
D. Sculley
CIKM
2008
Springer
13 years 9 months ago
Efficient and effective link analysis with precomputed salsa maps
SALSA is a link-based ranking algorithm that takes the result set of a query as input, extends the set to include additional neighboring documents in the web graph, and performs a...
Marc Najork, Nick Craswell