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Deep Exploration for Recommendation Systems

2021-09-26 06:54:26
Zheqing Zhu, Benjamin Van Roy
arXiv_AI
     
Abstract

We investigate the design of recommendation systems that can efficiently learn from sparse and delayed feedback. Deep Exploration can play an important role in such contexts, enabling a recommendation system to much more quickly assess a user's needs and personalize service. We design an algorithm based on Thompson Sampling that carries out Deep Exploration. We demonstrate through simulations that the algorithm can substantially amplify the rate of positive feedback relative to common recommendation system designs in a scalable fashion. These results demonstrate promise that we hope will inspire engineering of production recommendation systems that leverage Deep Exploration.

Abstract (translated)

URL

https://arxiv.org/abs/2109.12509

PDF

https://arxiv.org/pdf/2109.12509.pdf

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