Deep recommendation system
회차 | 일시 | 내용 | 발표자 | 자료 |
---|---|---|---|---|
1 | 12/14 | (책) 1. Brave New Realtime World : Introduction | 김성동 | |
(책) 2. Strange Recommendations? On the Weaknesses of Current Recommendation Engines | 김성동 | |||
(논문) Deep Reinforcement Learning in Large Discrete Action Spaces | 김무성 | |||
2 | 12/21 | (책) 3. Changing Not Just Analyzing: Control Theory and Reinforcement Learning (1) | 김성근 | |
(논문) Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions | 김무성 | |||
3 | 12/28 | (책) 3. Changing Not Just Analyzing: Control Theory and Reinforcement Learning (2) | 김성근 | |
(책) 4 Recommendations as a Game: Reinforcement Learning for Recommendation Engines | 김무성 | |||
(논문) Wide & Deep Learning for Recommender Systems | 김성동 | |||
(논문) Deep Neural Networks for YouTube Recommendations | 김성동 |
- Deep Reinforcement Learning in Large Discrete Action Spaces
- Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
- Wide & Deep Learning for Recommender Systems
- Deep Neural Networks for YouTube Recommendations
- Building and Evaluating an Adaptive Real-time Recommender System
- Conversational Recommendation System withUnsupervised Learning
- Autonomous CRM Control via CLV Approximation with Deep Reinforcement Learning in Discrete and Continuous Action Space
- Neural Network Based Next-Song Recommendation
- Off-policy evaluation for slate recommendation
- Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
- A Neural Autoregressive Approach to Collaborative Filtering
- Restricted Boltzmann Machines for Collaborative Filtering
- An MDP-Based Recommender System