Using causal Inference to better understand the search intent

Using causal inference to
better understand the search
intent.
Dateme Tubotamuno | SemanticGeek
SLIDESHARE.NET/DATEMETUBOTAMUNO
@DATEMET

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@datemeT #BrightonSEO
The
search
intent is
complex

Complex
like the
Theory of
Yawning

Search
Intent
&
User
Intent
are
Identical

Is Sometimes
Mistaken for
Query
Classification
Intent
Classification

CROCODILE
ALLIGATOR

Classification of
Location Queries

E.g: How do you get
to Jersey from
london
Source: https://www.vs.inf.ethz.ch/edu/SS2006/DS/slides/01_location_models.pdf
1
E.g: Where is the
Brighton Centre?
Position Queries

E.g: How do you get
to Jersey from
london
2
E.g: Hotels near
Brighton Centre?
Nearest Neighbour Queries

E.g: How do you get
to Jersey from
london
3
E.g: How do you get to
Brighton from London?
Navigation Queries

E.g: How do you get
to Jersey from
london
4
E.g: Caesars Palace top
floor suite ?
Range Queries

Source: https://www.thinkwithgoogle.com/consumer-insights/consumer-journey/i-want-to-go-micro-
moments/

or
Intent Classification
Intent Mining?

Intent ‘What’
Intent ‘Why’

Some sources for intent mining
CRM

Source: Trey Grainger: https://bit.ly/3ArGoLJ
Dimensions of User Intent
Content
Understanding
User
Understanding
Domain
Understanding

Content Understanding

User Understanding

Domain Understanding

"Search is far from a solved Problem"
Pandu Nayak - Head of Search Ranking, Google
https://www.youtube.com/watch?v=tFq6Q_muwG0

SEARCH INTENT
IS INDEED COMPLEX

Poor result for ‘homely animals.’

Google Translate is not right for query

CAUSAL
INFERENCE

The power of ‘Cause and Effect’

Cause and Effect
Causal
Inference
Causal
Calculus
https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-
de2cde8ea599
Inferring Causes
from data

Source: https://github.com/commonsense/conceptnet5/wiki/Relations
P(x|do(y))
i.e. the probability of x given that y is done

Cause
Effect
Cause
THE SEARCH INTENT IS LIKE A SEQUENCE
Purpose
HasEvent
Search Query
HasFirstSubevent
Action
HasLastSubevent

Estimation of Unobserved Quantities

CI questions around a vacation query
Source: https://www.kdnuggets.com/2020/08/microsoft-dowhy-framework-causal-inference.html
Would we
have fun?
How would we
feel after?
What should we
do on vacation?

Observing
effects of
going on
vacation
Or not
going on
vacation.
NOT BOTH

Confounder
Result/
Action
Search
Query
Intent
Confounder is a common cause
for cause and effect

E.g. of CAUSE relation on ConceptNet

Intent as a CAUSE relation

Quora as an intent research resource

List of Keywords that converted

Dimension One of the User Intent

Dimension Two of the User Intent

Dimension Three of the User Intent

Causal
Inference
in
Action

Definition of variables

Modules Installation

Reading of the CSV Table in
Pandas

Generating a Causal Graph View

Generating a Causal Estimate

A Tiptoe into
Intent Analysis
using Causal
Inference.

More Intent
Analysis are
required in our
Industry.

Beyond > Transactional, Navigational
and Informational

Resources
● https://medium.com/analytics-vidhya/causal-inference-an-introduction-
f424df7c76ef
● https://medium.com/data-science-at-microsoft/causal-inference-part-1-of-3-
understanding-the-fundamentals-816f4723e54a
● https://www.kdnuggets.com/2020/08/microsoft-dowhy-framework-causal-
inference.html
● https://github.com/microsoft/EconML/blob/master/notebooks/CustomerSc
enarios/Case%20Study%20-
%20Customer%20Segmentation%20at%20An%20Online%20Media%20Com
pany%20-%20EconML%20%2B%20DoWhy.ipynb
● https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-
causal-inference/
● https://www.slideshare.net/treygrainger/balancing-the-dimensions-of-user-
intent

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