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ECIR
2009
Springer
14 years 4 months ago
Mean-Variance Analysis: A New Document Ranking Theory in Information Retrieval
Abstract. This paper concerns document ranking in information retrieval. In information retrieval systems, the widely accepted probability ranking principle (PRP) suggests that, fo...
Jun Wang
ECIR
2011
Springer
12 years 10 months ago
Back to the Roots: Mean-Variance Analysis of Relevance Estimations
Recently, mean-variance analysis has been proposed as a novel paradigm to model document ranking in Information Retrieval. The main merit of this approach is that it diversifies t...
Guido Zuccon, Leif Azzopardi, Keith van Rijsbergen
SIGIR
2009
ACM
14 years 1 months ago
Portfolio theory of information retrieval
This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are depen...
Jun Wang, Jianhan Zhu
SIGIR
2002
ACM
13 years 6 months ago
A new family of online algorithms for category ranking
We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online learning algorithms. The a...
Koby Crammer, Yoram Singer
ECIR
2010
Springer
13 years 8 months ago
Using the Quantum Probability Ranking Principle to Rank Interdependent Documents
A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP)...
Guido Zuccon, Leif Azzopardi