google rankbrain: what it does, how it works, what it means by marcus tober

#SMX #22A @MarcusTober

Google RankBrain

-‐ WHAT IT DOES

-‐ HOW IT WORKS

-‐ WHAT IT MEANS

MARCUS TOBER

SMX West March 2, 2016


Searchmetrics Made with love in Berlin

More than 220 passionate people

Innovator in SEO Software since 2005


Marcus Tober Founder and CTO of Searchmetrics

In love with SEO and SEARCH since 2001

Study of computer science In Berlin, so I´m the Techie!


Machine Lear n ing or A I?


Machine Learning

(ML) ≠ AI


Machine Learning (ML)

Deep Learning Artificial Intelligence

An algorithm that improves over

time

Aims to bridge the gap between ML and AI – solves more complex

problems

Human like intelligence

Spectrum of Intelligence


•  Facebook photo recognition

•  Email spam filters

•  Database mining

•  Music or movie recommendations

iTunes / Spotify / Netflix

•  Solving games (e.g. chess) – IBM‘s Deep

Blue vs Garry Kasaparov

Common Applications of Machine Learning


•  More than 2,500 years old

•  Played by 40m worldwide

•  Deemed uncrackable by Machine

Learning/AI (until AlphaGo)

Go – The limits of machine learning


Is Go really that hard? (well actually, yes…)

1050 4x1079 – 4x1081 10171

Number of...

in a chess game in the observable universe in game of Go

…possible moves …possible moves …atoms


Not so simple

That’s…

1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000

…possible positions!


•  Traditional A.I methods -‐ which analyze all

possible positions –failed

•  AlphaGo users uses deep neural networks

across 12 different network layers

•  One neural network, selects the next move to

play. The other neural network, the “value

network”, predicts the winner of the game.

Deep learning - AlphaGo


Unders tand ing RankBra in


Legend in Neural Network Research. World renowned A.I. researcher.

Invented some of the core algorithms of Deep Learning back in the 80s

Working with Google

since 2011

New novel concept:

Thought Vectors

Geoffrey Hinton

Capturing thoughts…


IMAGINE EMPTY SPACE


Every word gets a position in space

Oregon

Salem

Sacramento

California

Visualizing RankBrain


Query sentences get a position in space.

What’s the weather going to be like in California?

Weather Forecast California


Using training data, similar query sentences (with similar results) are closely positioned


Queries and results get a position in space

Q

Result Scoring

R R

Good results rank better based on proximity



Traditonal Ranking Factors can no longer

make sense of organic rankings.

Searchmetrics Hypothesis


Searchmetrics Hypothesis

We said that:

•  RankBrain concentrates on relevant content

•  RankBrain uses thought vectors to map relevant

results to queries

•  This relevance score is then used to help order rankings

Q

R R


Brief study background

•  Top 30 Results for Google US

•  ~400,000 datapoints

•  3 keyword sets: Loan, E-‐Commerce, Health

•  Approach: Discover which ranking factors are

most important

•  Emulate RankBrain by adding scores to

understand the content relevance.

http://www.google.com/

Cash advance fresno ca

Examples: Backlinks & Internal Links

https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/

Rank: 5 Rank: 12

17 backlinks 21 internal links

52,000 backlinks 443 internal links

Keyword: “Cash advance fresno ca” LOAN


0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

E-Commerce Health Loan

Backlinks

Positive correlation

Negative correlation


0

50

100

150

200

250

300

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29


Number of Internal Links


fast cash credit card


Rank: 6 Rank: 17

5,685 Backlinks No KW in Title

59,780 Backlinks KW in Title

Keyword: “fast cash credit card” LOAN

Keyword in Title


0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29


Keyword in Title

10% of pages in “loans” only have the keyword in title-‐tag


natural detox

https://www.maakeentaart.nl/swiss-meringue-botercreme-maken-en-andere-botercreme/


Rank: 5 Rank 26

Word count = 3180 Internal links count = 375 Interactive elements = 398


Keyword: “natural detox” HEALTH

Word Count & Interactive Elements


1,000

1,500

2,000

2,500

3,000

3,500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29


Word Count


0

50

100

150

200

250

300

350

400

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29


Number of Interactive Elements


Why do “traditional” ranking factors fail to explain these examples?


•  We emulated RankBrain and gave search

results a relevance score Q

R

R

•  This score is based on how relevant a result is to

a query

•  We used around 25 relevance ranking factors

to assess relevance

Relevance Ranking Factors


•  9 out of top 10 e-‐commerce

websites for this keyword have

an add to cart function above

the fold

Keyword: “security camera system” E-‐COMM

Introducing Relevance


Keyword: “security camera system” E-‐COMM

•  Rank 9 does not.

•  However, it has the highest

relevance score of the top 30 -‐ and

thats why it ranks.



Best bluetooth headphones


Rank: 2 Rank 26

Internal links count = 170 Relevance = Highest in Top 30

Internal links count = 412 Relevance = low

Keyword: “best bluetooth headphones” E-‐COMM



natural detox


Rank: 5 Rank 26



Keyword: “natural detox” HEALTH


Content with a high relevance score:

•  matches user intention

•  Is logically structured and

comprehensive

•  Offers a good user experience

•  Deals with topics holistically

Being Relevant



•  Top ranking factors are different depending on

keyword set

Relevance Ranking

Factor

Traditional Ranking

Factor

Top 10 Ranking Factors by Category

•  Relevance ranking factors dominate results

across all keywords sets

•  All previous examples can be explained by

having a higher relevance score

•  This score overpowered other ranking factors,

meaning these pages ranked highly

Key Findings


Out look : th i s i s where we

are go ing


•  SEO is as important as ever, but it’s changing

T h i s i s whe r e

we a r e g o i n g

•  RankBrain is not used on all queries: for example

short/popular queries with well known results

are not filtered by RankBrain your content

matches user/query intent

•  Relevance is crucial for good rankings – RankBrain can detect how relevant your content

is

•  Make sure your content matches user/query

intent

Outlook for SEO


The Future of Search: An abundance of redundance

•  Incremental improvements through powerful

data insights

Machine learning and Searchmetrics Share a Philosophy:

•  Using Machine and Deep Learning to make

sense of complex data.

•  A data driven approach is only way to make

sense of the abundance of redundance online.

•  This applies for content, too.


Thank you for your attention!


SEE YOU AT THE NEXT #SMX!

THANK YOU!

google rankbrain: what it does, how it works, what it means by marcus tober

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