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LETOR: A benchmark collection for research on learning to rank for information retrieval

9 years 11 months ago
LETOR: A benchmark collection for research on learning to rank for information retrieval
LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation. We then compare several state-of-the-art learning to rank algorithms on LETOR, report their ranking performances, and make discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting research projects on learning to rank and related topics. Keywords Learning to rank · information retrieval · ...
Tao Qin, Tie-Yan Liu, Jun Xu, Hang Li
Added 28 Jan 2011
Updated 28 Jan 2011
Type Journal
Year 2010
Where IR
Authors Tao Qin, Tie-Yan Liu, Jun Xu, Hang Li
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