BEHAVIORAL TARGETING IN ON-LINE ADVERTISING: AN EMPIRICAL STUDY

AUTHORS:JOANNA JAWORSKAMARCIN SYDOWIN DEFENSE: XILING SUN & ARINDAM PAUL

INTRODUCTION Internet Economy is driven by Advertising

Search-based Ads(40%) Display Ads (22%) Classifieds (17%)

The revenue comes from whether user is to click on a ad or not Depends on degree of match between ad and

user' s context This kind of matching is called “targeting”

and forms a motivation for this paper

BEHAVIORAL TARGETING We need to automatically decide based

on the statistics of the users' web browsing history

Behavioral Targeting has a great potential in improving the performance of ad system

Experiments in this paper do not constitute any serious threat on users' privacy User represented by cookies

The General Model Each user is identified by a cookie and a

set of attributes U U: 13 different web page categories Each visit of the web page will increase the

corresponding category by 1

The format of some rows of profile data:

The General Model A model that can be represented as function

fc(U) = p [0, 1]∈ The potential relevance of the ad c presented to the user

described by the profile U. Decision whether to present the ad c to a user visiting the page fc(U) > θc, for some threshold θc which can be tuned

experimentally. Current model is simple. Only a single ad is considered at a

time

CTR (click-through rate) is used to evaluate performance higher CTR of the presented ad, the higher revenue of the ad-

serving system

Design of Experiments data comes from real impressions of ads

different data processing

Design of Experiments different Machine-Learning algorithms

different evaluation metrics

Design of Experiments Recall and Precision

Consider an example information request I (of a test reference collection) and its set R of relevant documents.

Let A be the answer set generated by retrieval strategy. Let |Ra| be the number of documents in the intersection of the sets

R and A Recall is the fraction of the relevant documents (the set R) which

has been retrieved, i.e. Recall = |Ra| / |R| Precision is the fraction of the retrieved documents (the set A)

which is relevant, i.e. Precision = |Ra| / |A|

Experimental Results Comparison of Various Algorithms and

Attribute Transformations

Experimental Results The Choice of the Training Sample

10%all − 1 − smp0 10%all 20%all

Experimental Results Observations

it is hard to find any clear relationship between the classification algorithm or data preprocessing technique applied and the performance.

the applied model of adaptive behavioral targeting seems to be generally successful

Different training set did not influence result

Contributions present an experimental framework for testing

and evaluating various factors propose a general adaptive behavioral

targeting model which is generally successful in practice

a preliminary comparison between a couple of classification algorithms and attribute-preprocessing techniques is made and reported

the evaluation is made on unique, large industrial datasets, the first reported evaluations made on real datasets

Conclusions although a very simple model, this model is nonetheless

successful It generally increase the precision value (hit rate).

no clear conclusion about which algorithms are better this is the initial work at this area

decide whether to present a single ad an obvious simplification of the real situation plan to extend the model to take into account multiple as

candidates

this work provides clear directions which all have formed foundations for future work

Further Work introduce temporal dimension additional category-based attributes

specifying the times spent on each of the categories (work-days and week-days)

introduce 2-fold profile : long & short term clustering users or advertising different (larger and balanced) training set extend the model such that it endlessly

adapts to the users and their behavior

Impact on future research This is kind of a seminal work in the area of

Behavioral Targeting in Advertising. It has motivated many future works in this direction

Tomarchio et al.'s work on developing data-driven behavioral algorithms for online ads is directly inspired from this work.

Trzcinski et al. also took cue from this paper on their work on analyzing privacy in mobile ads.

Wang et al.'s work on“Understanding Network and User-Targeting Properties of Web Advertising Networks”is also inspired from this work.

Thank you