There has been recent interest in collecting user or assessor preferences, rather than absolute judgments of relevance, for the evaluation or learning of ranking algorithms. Since measures like precision, recall, and DCG are defined over absolute judgments, evaluation over preferences will require new evaluation measures that explicitly model them. We describe a class of such measures and compare absolute and preference measures over a large TREC collection. Categories and Subject Descriptors: H.3.4 Information Storage and Retrieval; Systems and Software: Performance Evaluation General Terms: Performance, Measurement
Ben Carterette, Paul N. Bennett