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Personalization for web-based services using offline reinforcement learning
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AI
DOI：10.1007/s10994-024-06525-y.png)
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Abstract
En 中文
Large-scale Web-based services present opportunities for improving UI policies based on observed user interactions. We address challenges of learning such policies through offline reinforcement learning (RL). Deployed in a production system for user authentication in a major social network, it significantly improves long-term objectives. We articulate practical challenges, provide insights on training and evaluation of offline RL, and discuss generalizations toward offline RL's deployment in industry-scale applications.
Keywords:
Machine learning
Offline reinforcement learning
Decision-making
Web-based services
User authentication
Journal
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2.9
Papers:
2.6K
Citations:
3.4W
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