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...
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Abstract. Information retrieval systems have traditionally been evaluated over absolute judgments of relevance: each document is judged for relevance on its own, independent of oth...
Ben Carterette, Paul N. Bennett, David Maxwell Chi...
We present the results of a large-scale, end-to-end human evaluation of various sentiment summarization models. The evaluation shows that users have a strong preference for summar...
Kevin Lerman, Sasha Blair-Goldensohn, Ryan T. McDo...
Abstract—A probabilistic kernel approach to pairwise preference learning based on Gaussian processes is applied to predict preference judgments for sound quality degradation mech...
Perry Groot, Tom Heskes, Tjeerd Dijkstra, James M....