# Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model

Tang Weijing
Cited by: 0|Bibtex|Views95

Abstract:

We consider the problem of listwise learning-to-rank (LTR) on data with \textit{partitioned preference}, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown. The Plackett-Luce (PL) model has been widely used in listwise LTR methods. However, given $N$ items with \$M...More

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