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Realtime Data Mining Self-Learning Techniques for Recommendation Engines /
Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Birkhäuser,
2013.
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| Edición: | 1st ed. 2013. |
| Colección: | Applied and Numerical Harmonic Analysis,
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| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 1 Brave New Realtime World - Introduction
- 2 Strange Recommendations? - On The Weaknesses Of Current Recommendation Engines
- 3 Changing Not Just Analyzing - Control Theory And Reinforcement Learning
- 4 Recommendations As A Game - Reinforcement Learning For Recommendation Engines
- 5 How Engines Learn To Generate Recommendations - Adaptive Learning Algorithms
- 6 Up The Down Staircase - Hierarchical Reinforcement Learning
- 7 Breaking Dimensions - Adaptive Scoring With Sparse Grids
- 8 Decomposition In Transition - Adaptive Matrix Factorization
- 9 Decomposition In Transition Ii - Adaptive Tensor Factorization
- 10 The Big Picture - Towards A Synthesis Of Rl And Adaptive Tensor Factorization
- 11 What Cannot Be Measured Cannot Be Controlled - Gauging Success With A/B Tests
- 12 Building A Recommendation Engine - The Xelopes Library
- 13 Last Words - Conclusion
- References
- Summary Of Notation.