Intent graph: Deep analysis into search behaviour

Intent Graph: Deep Analysis
into Search Behaviour
Dateme Tubotamuno // SemanticGeek
SLIDESHARE.NET/DATEMETUBOTAMUNO
@DatemeT

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Theory
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Knowledge &
Reasoning
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Reasoning
Arsenal
F.C
Intent
Graph
Semantic Geek

@datemeT #BrightonSEO
User Intent is usually Oversimplified

Intents from
the Eyes of
Our Agenda

Seldom from that of the user

User Intent is in the WHY and not the WHAT

Intent should start with the WHY
Occasionally Fantasize of the ‘HOW’
e.g
TRANSACTIONAL
QUERIES
Often Romanticise about the ‘WHAT’
Frequently minimise the ‘WHY’
e.g
BERT
RANKBRAIN
e.g
MOTIVATIONS
REASONING

INTENT GRAPH

INTENT GRAPH
REASONING MOTIVATION

Source: https://www.gartner.com/en/documents/2081316

REASONING

:
Reasoning is at the 3rd LEVEL of cognition

Source: https://homes.cs.washington.edu/~msap/acl2020-commonsense/slides/01%20-%20Intro.pdf

REASONING
LOWER ORDER
INTENT INTUITION
HIGHER
ORDER
INTENT

When intent goes relational

Deepmind joins the relational reasoning party
https://deepmind.com/blog/article/neural-approach-relational-reasoning

Comparing relative merits between two fruits
https://deepmind.com/blog/article/neural-approach-relational-reasoning
KIWI FRUIT MANGO

1 2 3 4
Current Intent Analysis = Lower Order
(WHAT)

Micro Moments = Lower Order Intent (WHAT)

Understanding
intent is key in
search

Relational Reasoning aids higher-order
intent

The deep intent behind our search
Is linked to our experiences & stories

Understanding
intent should be
contextual

@datemeT
#BrightonSEO
A BLANKET APPROACH
IS COZY BUT SHALLOW

Understanding
intent is harder
than you think

My wife usually utters: “Honey the bin”
What’s her intent I ask myself?
Take the bin down? wet + dry waste?
The bin stinks?
Put some bin liner?
Bin area is untidy?
Brush up the area?

Google misunderstood my intent

Relational
reasoning
aids decision
science
Drawing up
links from our
experiences

Progression to relational reasoning
Percepts
Relational
Thinking
(Lower Order)
Relational
Reasoning
(Higher Order)
Fleeting Locus - World
Sensations
In the moment
Sensed
Effortless
Locus - Mind
Overtime
Examined
Effortful
Extrinsic Intrinsic
https://www.nature.com/article
s/npjscilearn20164/tables/1

Reasoning is a pivotal part of the intent graph

MOTIVATION

What are the
motivations
behind the
query?

Can you visualise
the possible
motivations?

Maslow’s Theory of Human Motivation
Source: https://www.simplypsychology.org/maslow.html

ERG Theory condenses Maslow's 5 Needs
Source:https://expertprogrammanagement.com/2018/10/erg-theory-of-motivation/

These
motivations
are behind
most queries.

Source: Vered Schwartz - Commonsense Reasoning for NLP: https://www.youtube.com/watch?v=InIffoMnV7k
KNOWLEDGE
OF
‘WHAT’
KNOWLEDGE
OF
‘WHY’ & ‘HOW’
Taxonomic Knowledge (A is A B)
Hierarchical Relationships
Reasoning - Causes and Effects
Semantic Relationships

THE INTENT LAB:
EXPERIMENT
TIME

https://github.com/EricFillion/happy-transformer
TOOLS
● Python
● Happy Transformer (BERT)
● Google Colab
● Github
● NetworkX
● MatplotLib
● Scikit-Learn
● Pandas
● Numpy
● Pytorch
● Pytorch Geometric

YOU’VE JUST WON A
NEW GYM CLIENT OR
EMPLOYED BY ONE

TIME TO BUILD AN
INTENT GRAPH
FOR THIS CLIENT
OR EMPLOYER.
STARTING WITH
THE USERS’ WHY!

Motivations or Reasons for Joining a Gym
Good for one’s health Reduces Anxiety
Meet like-minded people Enhances mood
Access to knowledge Good stress buster
Lose weight
Improve sleep
Access to equipment

Intents could be masked adjacent to a
query

LIKE LANGUAGE
MODELS SUCH
AS BERT,
ROBERTA &
XLNET MASKING
TOKENS

USE INTENT TO
PREDICT A
MASKED TOKEN
IN A BIGRAM
KEYWORD.
ENTER:
BERT, ROBERTA
& XLNET

The Gym Client/Employer Scenario
Lose weight
Enhance mood
ASSUMED USER INTENT Example Query BIGRAM HEAD TERM
I want to join a fitness
[MASK] to lose weight
and be happy
NGRAM 1: Fitness
NGRAM 2: [Mask]

Predicting the [Masked] word with BERT

Predicting the [Masked] word with RoBERTa

Predicting the [Masked] word with XLNET

● Best prediction
● Highest Softmax
● Highest search
volume
● Second best
prediction
● Second highest
search volume
● Worst prediction
● Lowest SoftMax
● Lowest Search
Volume
ROBERTA BERT XLNET

SERP Preview of the term: “Fitness Club”
Focus on
location-based
fitness
programs

SERP Preview of the term: “Fitness Group”
Focus on the
social
elements of
fitness
programs

BUILDING A WEIGHTED
INTENT GRAPH WITH
DIJKSTRA'S SHORTEST PATH
ALGORITHM

BASED ON ROBERTA’s
PREDICTION OF THE
MASKED TOKENS

USING THE SOFTMAX
SCORES AS THE EDGE
WEIGHTS

‘Fitness Club’ shortest path to the intent
https://github.com/gunner55/graph-intent/blob/main/Fitness_Intent_Graph.ipynb

Graph Representation of the search terms
https://github.com/gunner55/graph-
intent/blob/main/Fitness_Intent_Graph_Dra
w.ipynb

PYTORCH GEOMETRIC
(GRAPH NEURAL NETWORK)

https://github.com/gunner55/graph-
intent/blob/main/Fitness_Intent_Graph_Dra
w.ipynb
1-10 masked token predictions by RoBERTa 11-20 masked token predictions by RoBERTa

Toe dipping in Pytorch Geometric

INTENT-DRIVEN
MARKETERS START WITH
THE USERS’ WHY

INTENT IS AN
INWARD
EXPRESSION
OF THE USER

NOT AN OUTWARD
DESPERATION OF A
BUSINESS

STARING AT SERP WILL NOT GIVE YOU
A COMPLETE PICTURE OF INTENT

GOOGLE IS WORKING
HARD TO SOLVE SEARCH

“There is actually no end in sight in terms of
when this will actually be solved."
Ben Gomes - SVP, Google
"Search is far from a solved Problem"
Pandu Nayak - Head of Search Ranking, Google
https://www.youtube.com/watch?v=tFq6Q_muwG0

LET’S DIG DEEPER AS AN
INDUSTRY

Intent graph: Deep analysis into search behaviour

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