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Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these...
Listwise is an important approach in learning to rank. Most of the existing lisewise methods use a linear ranking function which can only achieve a limited performance being applied to complex ranking problem. This paper proposes a non-linear listwise algorithm inspired by boosting and clustering. Different from the previous listwise approaches, our algorithm constructs weak rankers through directly...
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