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Found 4 papers, 3 papers with code
Deep Neural Networks for YouTube Recommendations
YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence.
Top-K Off-Policy Correction for a REINFORCE Recommender System
The contributions of the paper are: (1) scaling REINFORCE to a production recommender system with an action space on the orders of millions; (2) applying off-policy correction to address data biases in learning from logged feedback collected from multiple behavior policies; (3) proposing a novel top-K off-policy correction to account for our policy recommending multiple items at a time; (4) showcasing the value of exploration.
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
(i) We develop SLATEQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates.
KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction
Predicting future trajectories of road agents is a critical task for autonomous driving.
