How to Spot a Bear - An Intro to Machine Learning for SEO

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@TomAnthonySEO
April 2015 - BrightonSEO
HOW TO SPOT A BEAR
A Machine Learning Introduction for SEOs

Can you define a list of
rules for spotting
bears?

1) Four legs.
Let’s start with:

Bear!

List of rules (first half):
(when I asked in the office)
1. Four legs.
2.Breathes.
3.Furry.
4.Long snout.

Bear!

List of rules:
1. Four legs.
2.Breathes.
3.Furry.
4.Long snout.
5.Brown.
6.Not always brown.
7. Mammal.
8.No tail.
(how do...

Let’s check our
rules…

Rules say:
Bear

Rules say:
Harmless Furry Thing
(less than 4 legs)

Rules say:
Odd Grey Creature
(no long snout)

Remove ‘long snout’, and rules say:
Bear
(Extra-terrestrial bear?!)

Our rules suck.

A different bear:
Google’s Panda

Can you define a list of
rules for spotting
spammy pages?
Same problem as
bears!

NBED GOOD PAGE
Good page

NBED GOOD PAGE
Commercial page, still good.

Hrm…

Seems legit…

WTF!

Google can’t write
rules.

What we can do is
identify spammy or
non-spammy
attributes.

Are there adverts on the page?
Are there lots of spelling mistakes?
Is there little text content?
Are there Calls To Actio...

Smooth segue to:
Machine Learning

List of pages we’ve
manually classified.
List of attributes that we
believe are important to
classifying pages.

adverts
on page?
more than 5
spelling
mistakes?
less than 200
words of
content?
CTA in ALL
CAPS?
site A Y Y Y Y Spam Site
...

Neural Networks: A Perceptron
Inputs
Output
Neuron

Neural Networks: A Perceptron
Inputs
Output
1
if:
inputs >= 1
output TRUE
0
1
0
0.5
0.5
0.5
0.5

1 x 0.5 = 0.5
0 x 0.5 = 0
1 x 0.5 = 0.5
0 x 0.5 = 0
1
______
Total:
Output: TRUE
1
if:
inputs >= 1
output TRUE
0
1
0
0.5
0...

1 x 0.5 = 0.5
0 x 0.5 = 0
0 x 0.5 = 0
0 x 0.5 = 0
0.5
______
Total:
Output: FALSE
1
if:
inputs >= 1
output TRUE
0
0
0
0.5
...

1 x 0.5 = 0.5
0 x 0.5 = 0
1 x 0.4 = 0.4
0 x 0.5 = 0
0.9
______
Total:
Output: FALSE
1
if:
inputs >= 1
output TRUE
0
1
0
0....

adverts
on page?
more than 5
spelling
mistakes?
less than 200
words of
content?
CTA in ALL
CAPS?
site A Y Y Y Y Spam Site
...

Untrained Neuron
Is site
spam?
adverts
>5 spelling
mistakes
< 200 words
content
CTA in ALL
CAPS
if:
inputs >= 1
output TRU...

Training
adverts
>5 spelling
mistakes
< 200 words
content
CTA in ALL
CAPS
if:
inputs >= 1
output TRUE
0.5
0.5
0.5
0.5
0
0
...

Training
adverts
>5 spelling
mistakes
< 200 words
content
CTA in ALL
CAPS
if:
inputs >= 1
output TRUE
0.5
0.5
0.6
0.6

After training: 4/5 sites correct
Is site
spam?
adverts
>5 spelling
mistakes
< 200 words
content
CTA in ALL
CAPS
if:
input...

ANNs typically have many neurons
source: http://www.teco.edu/~albrecht/neuro/html/node18.html

Deep Learning

Humans are good at pattern matching

We’re better than machines…
source: Pawan Sinha (http://web.mit.edu/bcs/sinha/papers/sinha_recog_review_NN.pdf)

ML can learn to recognise cats from examples

Deep Learning learns more like us

Ok, so what does this have to
do with Google?

PandaML based algorithm updates

Old index Caﬀeine
Caffeine - Infrastructure Update
(we believe this made Panda+Penguin possible)

Hummingbird is to ???
as
Caffeine is to Panda+Penguin

Hummingbird
Is it similar to Caffeine? Is it the basis for new natural language algorithms?

Where is Google
going next with ML?

Idea
Image Search 2.0

Image Labelling

Image Labelling

Video Labelling

ML Generated Image Descriptions
“Two pizzas sitting on top of a stove top oven”

Natural Language
Faceted Search
Idea

‘show me olympic athletes' ‘show me the women'

“Find well rated vegetarian cooking
books written after 1990”
How about:

Idea
Factual Accuracy
as a
Ranking Factor

Fact CheckingKnowledge Vault

Idea: Bad Facts
NBED- shot of Google
talking about this shit
Estimating ‘Trustworthiness’

Idea
Entirely ML Generated
Algorithm?

http://dis.tl/ml-algo

Thanks! :)
@TomAnthonySEO

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