Intro to Factorization Machines

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Pavel Kalaidin

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

Daten & Analysen

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?

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

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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