Realtime Data Mining Self-Learning Techniques for Recommendation Engines /

• Κύριοι συγγραφείς: Paprotny, Alexander (Συγγραφέας), Thess, Michael (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Birkhäuser, 2013.
• Σειρά: Applied and Numerical Harmonic Analysis,
• Θέματα: Mathematics.; Computer science > Mathematics.; Computer mathematics.; Computer software.; Computational Science and Engineering.; Mathematical Applications in Computer Science.; Mathematical Software.

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...

Πίνακας περιεχομένων:

• 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.