2 papers
cs.DS2026
Kd-tree Based Wasserstein Distance Approximation for High-Dimensional Data
Kanata Teshigawara, Keisho Oh, Ken Kobayashi +1
The Wasserstein distance is a discrepancy measure between probability distributions, defined by an optimal transport problem. It has been used for various tasks such as retrieving…
cs.IR2023
An IPW-based Unbiased Ranking Metric in Two-sided Markets
Keisho Oh, Naoki Nishimura, Minje Sung +2
In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such…