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Personalization for Web-based Services using Offline Reinforcement Learning (2102.05612v1)

Published 10 Feb 2021 in cs.LG, cs.HC, and cs.SE

Abstract: Large-scale Web-based services present opportunities for improving UI policies based on observed user interactions. We address challenges of learning such policies through model-free offline Reinforcement Learning (RL) with off-policy training. Deployed in a production system for user authentication in a major social network, it significantly improves long-term objectives. We articulate practical challenges, compare several ML techniques, provide insights on training and evaluation of RL models, and discuss generalizations.

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