2017 OffPolicyEvaluationforSlateReco

• (Swaminathan et al., 2017) ⇒ Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, and Imed Zitouni. (2017). “Off-policy Evaluation for Slate Recommendation.” In: Proceedings of Advances in Neural Information Processing Systems 30 (NIPS-2017).

Subject Headings:

Notes

• https://papers.nips.cc/paper/6954-off-policy-evaluation-for-slate-recommendation

Cited By

• http://scholar.google.com/scholar?q=%222017%22+Off-policy+Evaluation+for+Slate+Recommendation

Quotes

Abstract

This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context --- a common scenario in web search, ads, and recommendation. We build on techniques from combinatorial bandits to introduce a new practical estimator. A thorough empirical evaluation on real-world data reveals that our estimator is accurate in a variety of settings, including as a subroutine in a learning-to-rank task, where it achieves competitive performance. We derive conditions under which our estimator is unbiased --- these conditions are weaker than prior heuristics for slate evaluation --- and experimentally demonstrate a smaller bias than parametric approaches, even when these conditions are violated. Finally, our theory and experiments also show exponential savings in the amount of required data compared with general unbiased estimators.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 OffPolicyEvaluationforSlateReco	John Langford Adith Swaminathan Akshay Krishnamurthy Alekh Agarwal Miro Dudik Damien Jose Imed Zitouni			Off-policy Evaluation for Slate Recommendation						2017