Statistical Challenges in Computational Advertising

author: Deepayan Chakrabarti, Carnegie Mellon University
author: Deepak Agarwal, Yahoo! Research
published: Sept. 14, 2009,   recorded: June 2009,   views: 2347
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 1:25:55
!NOW PLAYING
Watch Part 2
Part 2 1:29:31
!NOW PLAYING

Description

Many organizations now devote significant fractions of their advertising/outreach budgets to online advertising; ad-networks like Yahoo!, Google, MSN have responded by constructing new kinds of economic models and perform the fundamental task of matching the most relevant ads (selected from a large inventory) for a (query,user) pair in a given context. Nearly all of the challenges that arise are substantially data- or model-driven (or both). Computational Advertising is a relatively new scientific sub-discipline at the interesection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, optimization and microeconomics that address this match-making problem and provides unprecedented opportunities to data miners.

Topics covered include a comprehensive introduction to several advertising forms (sponsored search, contextual adverting, display advertising), revenue models (pay-per-click, pay-per-view, pay-per-conversion) and data mining challenges involved, along with an overview of state-of-the-art techniques in the area with a detailed discussion of open problems. We will cover information retrieval techniques and their limitations; data mining challenges involved in performing ad matching through clickstream data and challenging optimization issues that arise in display advertising. In particular, we will cover statistical modeling techniques for clickstream data and explore/exploit schemes to perform online experiments for better long-term performance using multi-armed bandit schemes. We also discuss the close relationship of techniques used in recommender systems to our problem but indicate several additional issues that needs to be addressed before they become routine in computational advertising.

We will only assume basic knowledge of statistical methods, no prior knowledge of online advertising is required. In fact, the first hour that provides an introduction to the area would be appropriate for all registered attendees of KDD 2009. The second half would require familiarity with basic concepts like regression, probability distributions and appreciation of issues involved in fitting statistical models to large scale applications. No prior knowledge of multi-armed bandits would be assumed.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: