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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines by Alexander Paprotny, Michael Thess (auth.) is a nonfiction available to read on EtoBox.

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ANHA Series Preface 6 Preface 12 Acknowledgements 15 Contents 16 Summary of Notation 20 Chapter 1: Brave New Realtime World: Introduction 25 1.1 Historical Perspective 25 1.2 Realtime Analytics Systems 26 1.3 Advantages of Realtime Analytics Systems 27 1.4 Disadvantages of Realtime Analytics Systems 28 1.5 Combining Offline and Online Analysis 30 1.6 Methodical Remarks 30 Chapter 2: Strange Recommendations? On the Weaknesses of Current Recommendation Engines 34 2.1 Introduction to Recommendation Engines 34 2.2 Weaknesses of Current Recommendation Engines and How to Overcome Them 35 Chapter 3: Changing Not Just Analyzing: Control Theory and Reinforcement Learning 38 3.1 Modeling 39 3.2 Markov Property 40 3.3 Implementing the Policy: Selecting the Actions 41 3.4 Model of the Environment 42 3.5 The Bellman Equation 43 3.6 Determining an Optimal Solution 47 3.7 The Adaptive Case 49 3.8 The Model-Free Approach 51 3.9 Remarks on the Model 54 3.9.1 Infinite-Horizon Problems 54 3.9.2 Properties of Graphs and Matrices 55 3.9.3 The Steady-State Distribution 56 3.9.4 On the Convergence and Implementation of RL Methods 58 3.10 Summary 63 Chapter 4: Recommendations as a Game: Reinforcement Lear

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Author

Alexander Paprotny, Michael Thess (auth.)

Publisher

Birkhäuser

Published

2013

Language

EN

Category

nonfiction