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Intro to Factorization Machines

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Pavel Kalaidin
Pavel Kalaidin

Introduction to Factorization Machines, talk at St. Petersburg Data Science Meetup #2, May, 29th, 2015

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Factorization Machines St. Petersburg Data Science Meetup, May, 29th, 2015 @facultyofwonder Rutarget/Segmento
credits: https://twitter.com/ejlbell/status/559772240544563201
Q: What, say, 3 recent papers in machine learning do you think will be influential to directing the cutting edge of research these days? Peter Norvig: I’ve never been able to pick lasting papers in the past, so don’t trust me now, but here are a few: ● Rendle’s “Factorization Machines” ● Wang et al. “Bayesian optimization in high dimensions via random embeddings” ● Dean et al. “Fast, Accurate Detection of 100,000 Object Classes on a Single Machine” http://blog.teamleada.com/2014/08/ask-peter-norvig/
Criteo Dataset: http://labs.criteo.com/downloads/download-terabyte-click-logs/
Data Lots of categorical features Sparse settings Pairwise interactions Hashing trick?
Linear model
Polynomial features independent interactions
Factorization breaking the independence of interaction parameters
Example U = {Alice (A), Bob (B), Charlie (C), . . .} I = {Titanic (TI), Notting Hill (NH), Star Wars (SW), Star Trek (ST), . . .}
Example {(A, TI, 2010-1, 5),(A, NH, 2010-2, 3),(A, SW, 2010-4, 1), (B, SW, 2009-5, 4),(B, ST, 2009-8, 5), (C, TI, 2009-9, 1),(C, SW, 2009-12, 5)} Interaction between Alice and Star Trek to predict rating? Zero interaction? B-SW and C-SW are similar A and C are different ST and SW are similar A-SW and A-ST are to be similar
Complexity Number of parameters: 1 + p + k * p Linear to the input size and the size of factorization
Regularization Many parameters, prone to overfitting L2 regularization
LR+FM+SGD
Learning
Learning bit.ly/pyfm_demo
Hyperparameters Number of factors Regularization Learning rate Initial weights
FFM:ideas Features can be grouped into fields: users, movies, context, SSPs, publishers, whatever Better use this information Factor vector per field
Summary Factorized interactions High sparsity is OK for parameters estimation
Papers,papers bit.ly/factorization_machines_2010 bit.ly/libfm bit.ly/field_aware_FM

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