Traditional retrieval evaluation uses explicit relevance judgments which are expensive to collect. Relevance assessments inferred from implicit feedback such as click-through data...
Katja Hofmann, Bouke Huurnink, Marc Bron, Maarten ...
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...
This paper gives an overview of the evaluation method used for the Web Retrieval Task in the Third NTCIR Workshop, which is currently in progress. In the Web Retrieval Task, we tr...
Koji Eguchi, Keizo Oyama, Emi Ishida, Kazuko Kuriy...
Forming test collection relevance judgments from the pooled output of multiple retrieval systems has become the standard process for creating resources such as the TREC, CLEF, and...