Statistical Challenges in Computational Advertising
author: Deepak Agarwal, LinkedIn Corporation
published: Sept. 14, 2009, recorded: June 2009, views: 17708
Report a problem or upload filesIf 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.
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.
Download slides: kdd09_chakrabarti_agarwal_scca_01.ppt (2.1 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !
Reviews and comments:
Social marketing https://mediaonemarketing.com.sg/sing... is the activity of a company that benefits society and does not promise direct material benefits. Social marketing is usually part of a brand's global strategy. The actions and campaigns organized by the brand within the framework of social activity should intersect with the immediate sphere of the brand's activity or correlate with its philosophy and values.
This lecture contains really worthwhile facts that any student can use when writing his dissertation, as the experts from https://dissertationteam.com/ do, and this will surely guarantee an excellent result on the dissertation defense.
Cool, check also <a href="https://www.wikipedia.org/">this</a>
Cool, check also [url=https://www.wikipedia.org/]this[/url]
Cool, check also this https://www.wikipedia.org/
Write your own review or comment: