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An Offline Metric for the Debiasedness of Click Models

Click models aim to identify and extract biases in search data, but we do not evaluate whether they actually do so. We propose a metric based on relevance annotations that measures the debiasedness of click models, and consequently their usefulness in learning-to-rank from implicit feedback.

Evaluating the Robustness of Click Models to Policy Distributional Shift

We investigated how click models *actually* perform (spoiler: not so great), and whether our offline metrics capture this (spoiler 2: they don't). Here's what it means for ULTR researchers and practitioners.

Articles Tagged: "Click models"

An Offline Metric for the Debiasedness of Click Models

Evaluating the Robustness of Click Models to Policy Distributional Shift