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.
learning ranking preference combinational retrieval systems use logistic regression model documentation training data relevance voting algorithm information retrieval data fusion