Using PageRank to infer user preferences

Recently, researchers have shown interest in the use of preference judgments for evaluation in IR literature. Although preference judgments have several advantages over absolute judgment, one of the major disadvantages is that the number of judgments needed increases polynomially as the number of documents in the pool increases. We propose a novel method using PageRank to minimize the number of judgments required to evaluate systems using preference judgments. We test the proposed hypotheses using the TREC 2004 to 2006 Terabyte dataset to show that it is possible to reduce the evaluation cost considerably. Further, we study the susceptibility of the methods due to assessor errors.