24 research outputs found
Learning Classifiers under Delayed Feedback with a Time Window Assumption
We consider training a binary classifier under delayed feedback (DF
Learning). In DF Learning, we first receive negative samples; subsequently,
some samples turn positive. This problem is conceivable in various real-world
applications such as online advertisements, where the user action takes place
long after the first click. Owing to the delayed feedback, simply separating
the positive and negative data causes a sample selection bias. One solution is
to assume that a long time window after first observing a sample reduces the
sample selection bias. However, existing studies report that only using a
portion of all samples based on the time window assumption yields suboptimal
performance, and the use of all samples along with the time window assumption
improves empirical performance. Extending these existing studies, we propose a
method with an unbiased and convex empirical risk constructed from the whole
samples under the time window assumption. We provide experimental results to
demonstrate the effectiveness of the proposed method using a real traffic log
dataset