Realtime Data Mining pp 41-56 | Cite as
Recommendations as a Game: Reinforcement Learning for Recommendation Engines
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Abstract
We describe the application of reinforcement learning to recommendation engines. At this, we introduce RE-specific empirical assumptions to reduce the complexity of RL in order to make it applicable to real-live recommendation problems. Especially, we provide a new approach for estimating transition probabilities of multiple recommendations based on that of single recommendations. The estimation of transition probabilities for single recommendations is left as an open problem that is covered in Chap. 5. Finally, we introduce a simple framework for testing online recommendations.
Keywords
Recommendation Engine Reinforcement Learning Theory Multiple Recommendations Single Recommendation Estimated Transition Probabilities
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References
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