Learning a ranking from pairwise preferences

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. Categories and Subject Descriptors: H.3.3 Information Search and Retrieval: Search Process General Terms: Algorithms, Design, Experimentation, Performance

• Type: Conference
• Year: 2006
• Where: SIGIR
• Authors: Ben Carterette, Desislava Petkova
• Keywords: Logistic Regression Model, Multiple Retrieval Systems, Pairwise Document Preferences, SIGIR 2006