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Abstract:
We discuss the problem of ranking instances. In our framework
each instance is associated with a rank or a rating, which is an
integer from 1 to
k
. Our goal is to find a rank-prediction rule that assigns each
instance a rank which is as close as possible to the instance's
true rank. We describe a simple and efficient online algorithm,
analyze its performance in the mistake bound model, and prove its
correctness. We describe two sets of experiments, with synthetic
data and with the EachMovie dataset for collaborative filtering.
In the experiments we performed, our algorithm outperforms online
algorithms for regression and classification applied to
ranking.
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