BEHAVIORAL TARGETING IN ON-LINE ADVERTISING: AN EMPIRICAL STUDY AUTHORS: JOANNA JAWORSKA MARCIN SYDOW IN 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  f c (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  f c (U) > θ c, for some threshold θ c 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