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Oral
Off-policy evaluation for slate recommendation
Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni

Wed Dec 06 04:20 PM -- 04:35 PM (PST) @ Hall A
in Reinforcement Learning, Algorithms, Applications »

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.

Author Information

Adith Swaminathan (Microsoft Research)
Akshay Krishnamurthy (Microsoft)
Alekh Agarwal (Microsoft Research)
Miro Dudik (Microsoft Research)
John Langford (Microsoft Research New York)
Damien Jose (Microsoft)
Imed Zitouni (Microsoft)

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