IN DEFENSE: XILING SUN & ARINDAM PAUL

Behavioral Targeting in on-line advertising: AN EMPIRICAL STUDYAUTHORS:Joanna JAWORSKAMARCIN 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 • 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

IN DEFENSE: XILING SUN & ARINDAM PAUL