Learning a ranking from pairwise preferences

Authors:
Ben Carterette;Desislava Petkova
Affiliations:
University of Massachusetts Amherst, Amherst, MA;University of Massachusetts Amherst, Amherst, MA
Venue:
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2006

Citing 3
Cited 10

Automatic combination of multiple ranked retrieval systems

SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Models for metasearch

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A formal approach to score normalization for meta-search

HLT '02 Proceedings of the second international conference on Human Language Technology Research

Robust test collections for retrieval evaluation

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A support vector method for optimizing average precision

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive relevance feedback in information retrieval

Proceedings of the 18th ACM conference on Information and knowledge management
Ranking structured documents: a large margin based approach for patent prior art search

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Here or there: preference judgments for relevance

ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Research methodology in studies of assessor effort for information retrieval evaluation

Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Learning to rank for why-question answering

Information Retrieval
Competition-based user expertise score estimation

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A learning approach to optimizing exploration---exploitation tradeoff in relevance feedback

Information Retrieval
SRbench--a benchmark for soundtrack recommendation systems

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce a novel approach to combining rankings from multiple retrieval systems. We use a logistic regression model or an SVM to learn a ranking from pairwise document preferences. Our approach requires no training data or relevance scores, and outperforms a popular voting algorithm.