Realtime Data Mining

1. Brave New Realtime World: Introduction
Alexander Paprotny, Michael Thess

2. Strange Recommendations? On the Weaknesses of Current Recommendation Engines
Alexander Paprotny, Michael Thess

3. Changing Not Just Analyzing: Control Theory and Reinforcement Learning
Alexander Paprotny, Michael Thess

4. Recommendations as a Game: Reinforcement Learning for Recommendation Engines
Alexander Paprotny, Michael Thess

5. How Engines Learn to Generate Recommendations: Adaptive Learning Algorithms
Alexander Paprotny, Michael Thess

6. Up the Down Staircase: Hierarchical Reinforcement Learning
Alexander Paprotny, Michael Thess

7. Breaking Dimensions: Adaptive Scoring with Sparse Grids
Alexander Paprotny, Michael Thess

8. Decomposition in Transition: Adaptive Matrix Factorization
Alexander Paprotny, Michael Thess

9. Decomposition in Transition II: Adaptive Tensor Factorization
Alexander Paprotny, Michael Thess

10. The Big Picture: Toward a Synthesis of RL and Adaptive Tensor Factorization
Alexander Paprotny, Michael Thess

11. What Cannot Be Measured Cannot Be Controlled: Gauging Success with A/B Tests
Alexander Paprotny, Michael Thess

12. Building a Recommendation Engine: The XELOPES Library
Alexander Paprotny, Michael Thess

13. Last Words: Conclusion
Alexander Paprotny, Michael Thess

Keywords: Mathematics, Computational Science and Engineering, Mathematical Applications in Computer Science, Mathematical Software