Realtime data mining : self-learning techniques for recommendation angines
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...
Guardat en:
| Autor principal: | |
|---|---|
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
Cham :
Springer International Publishing
[20..].
Cham : Springer Nature |
| Edició: | 1st ed. 2013. |
| Col·lecció: | Applied and Numerical Harmonic Analysis
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| Accés en línia: | Accès sur la plateforme de l'éditeur (Springer) Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Realtime Data Mining, Texte imprimé, 9783319013206 • Realtime Data Mining, Texte imprimé, 9783319013220 • Realtime Data Mining, Texte imprimé, 9783319344454 |
Taula de continguts:
- 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