Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines

Realtime Data Mining: Self-Learning Techniques for Recommendation Engines

Paprotny, Alexander
Thess, Michael

Paperback

Series: Applied and Numerical Harmonic Analysis

ApplicationsGeneral ComputersGeneral Mathematics

ISBN10: 3319344455
ISBN13: 9783319344454
Publisher: Birkhauser
Published: Aug 27 2016
Pages: 313
Weight: 1.05
Height: 0.71 Width: 6.14 Depth: 9.21
Language: English

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.

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