TY - GEN
T1 - Pranking with ranking
AU - Crammer, Koby
AU - Singer, Yoram
PY - 2002/1/1
Y1 - 2002/1/1
N2 - 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 t o find a rank-prediction rule t hat 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 algorit hms for regression and classification applied t o ranking.
AB - 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 t o find a rank-prediction rule t hat 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 algorit hms for regression and classification applied t o ranking.
UR - http://www.scopus.com/inward/record.url?scp=84898958855&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84898958855
SN - 0262042088
SN - 9780262042086
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001
PB - Neural information processing systems foundation
T2 - 15th Annual Neural Information Processing Systems Conference, NIPS 2001
Y2 - 3 December 2001 through 8 December 2001
ER -