Taxonomy-based
Contextual Ads Targeting
Patrick Nicolas
Dec 8, 2009
patricknicolas.blogspot.com
www.slideshare.net/pnicola...
Purpose
This presentation is a short introduction
to the different components of an ads
server that leverages semantic ana...
Semantics
A few definitions…
Contextual targeting is the process of
inserting the most appropriate advertising
into a publ...
Conundrum
Should targeting relies on
● audience preferences & behavior history
● content topic and style
● both?
Publisher...
Taxonomy-based Targeting
It is assumed that the content
consumed by a visitor is reflective of
his/her
interests,
tastes
&...
Architecture
An ads targeting
components:

engine

two

key

● Optimizer
to
balance
objectives
(budget,
volume)
&
constrai...
Use Case
1. Campaign manager defines the objectives &
constraints
2. Optimizer computes the best promotion in the
inventor...
Semantic Analysis
1. The optimizer performs a semantic analysis of
both the content & promotional material.
1. The analysi...
Taxonomy Match
Portability
Music

Device

Travel

Content taxonomy
Content

Matching
Listening
Device
Device
iPod

Promoti...
Test Results
The taxonomy match algorithm was
evaluated against a rule-based targeting
engine
for
consumer
discretionary
p...
References
● How much can Behavioral Targeting Help Online
Advertising? J. Yan, N. Lu, G. Wang, W. Zhang, Y Jiang
http://w...
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Taxonomy-based Contextual Ads Targeting

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Brief overview of contextual targeting for advertisers using brand taxonomy and machine learning.

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Taxonomy-based Contextual Ads Targeting

  1. 1. Taxonomy-based Contextual Ads Targeting Patrick Nicolas Dec 8, 2009 patricknicolas.blogspot.com www.slideshare.net/pnicolas https://github.com/prnicolas
  2. 2. Purpose This presentation is a short introduction to the different components of an ads server that leverages semantic analysis to segment and target audience. Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  3. 3. Semantics A few definitions… Contextual targeting is the process of inserting the most appropriate advertising into a published content (web pages, social network, tweets, blogs..) Taxonomy is the study or science of classification of concept or concrete items, in a logical and repeatable manner. Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  4. 4. Conundrum Should targeting relies on ● audience preferences & behavior history ● content topic and style ● both? Publisher Content User Market Advertiser Promotion Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  5. 5. Taxonomy-based Targeting It is assumed that the content consumed by a visitor is reflective of his/her interests, tastes & demographic characteristics. Therefore, targeting (yield) consists of analyzing content & extracting context. Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  6. 6. Architecture An ads targeting components: engine two key ● Optimizer to balance objectives (budget, volume) & constraints (placements, frequency, exclusivity,..) ● Dispatcher to select the ad with the highest predicted yield according to content Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  7. 7. Use Case 1. Campaign manager defines the objectives & constraints 2. Optimizer computes the best promotion in the inventory that satisfy constraints 3. Dispatcher formats & dispatches the promotion Ads. inventory Campaign 1 1 Manager Optimizer 1 2 1 Dispatcher 3 Content Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  8. 8. Semantic Analysis 1. The optimizer performs a semantic analysis of both the content & promotional material. 1. The analysis generates taxonomy or semantic classification graphs. 1. Finally, the promotion with the taxonomy graph which is the closest to the content graph is selected Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  9. 9. Taxonomy Match Portability Music Device Travel Content taxonomy Content Matching Listening Device Device iPod Promotion Autonomy Promotion taxonomy Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  10. 10. Test Results The taxonomy match algorithm was evaluated against a rule-based targeting engine for consumer discretionary products. The Click Through Rate (CTR) increased from 0.8% to 0.193% (handbags) and 0.052% to 0.98% (upscale pen). Patrick Nicolas Copyright 2009-2011 - All rights reserved.
  11. 11. References ● How much can Behavioral Targeting Help Online Advertising? J. Yan, N. Lu, G. Wang, W. Zhang, Y Jiang http://www2009.eprints.org/27/1/p261.pdf ● Introduction to Semantic Analysis http://www.cs.tut.fi/sgn/arg/klap/introduction-semantics.pdf Patrick Nicolas Copyright 2009-2011 - All rights reserved.

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