Contents

• Course Information
• Course Schedule
• Lecture Handouts
• Readings
• Assignments
• Project
• Instructor Bios
• Related Courses
• Acknowledgement

Course Information

Overview 
Computational advertising is an emerging new scientific sub-discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, classification, optimization, and microeconomics. The central problem of computational advertising is to find the "best match" between a given user in a given context and a suitable advertisement. The context could be a user entering a query in a search engine ("sponsored search"), a user reading a web page ("content match" and "display ads"), a user watching a movie on a portable device, and so on. The information about the user can vary from scarily detailed to practically nil. The number of potential advertisements might be in the billions. Thus, depending on the definition of "best match" this problem leads to a variety of massive optimization and search problems, with complicated constraints, and challenging data representation and access problems. The solution to these problems provides the scientific and technical foundations for the $20 billion online advertising industry.

This course aims to provide a good introduction to the main algorithmic issues and solutions in computational advertising, as currently applied to building platforms for various online advertising formats. At the same time we intend to briefly survey the economics and marketplace aspects of the industry, as well as some of the research frontiers. The intended audience are students interested in the practical and theoretical aspects of web advertising.

The tentative list of topics include: The online advertising landscape; Marketplace and economics; Data representation and optimization challenges in online advertising; The information retrieval approach to textual ads selection; Sponsored search; Context match; Display advertising; Behavioral targeting; Emerging formats and technologies: mobile, aps, games, etc.

There are no formal prerequisites but some familiarity with the basic concepts of probability, economics, machine learning, and optimization is expected and good web skills are required. The course will likely include a "real life" project where students will have a budget to advertise for a certain business and will be required to analyze and justify their choices.

Teaching Staff: The best way to reach us is via email at: msande239-aut1112-staff@lists.stanford.edu
Instructors

• Andrei Broder, Yahoo! Research, broder@yahoo-inc.com
  Office Hours: By appointment
• Vanja Josifovski, Yahoo! Research, vanjaj@yahoo-inc.com
  Office Hours: By appointment

TA

• Krishnamurthy Iyer (kriyer AT stanford)
  Office hours: Tuesday, 6:00 -7:30pm, Huang 304

Meeting Time/Location
Fri 10 am-12:50 pm, Hewlett Teaching Center, Rm 101

Course Schedule

• 09/30 Overview and Introduction
• 10/07 Marketplace and Economics
• 10/14 Textual Advertising 1: Sponsored Search
• 10/21 Textual Advertising 2: Contextual Advertising
• 10/28 Display Advertising 1
• 11/04 Display Advertising 2
• 11/11 Targeting
• 11/18 Recommender Systems
• 12/02 Mobile, Video and other Emerging Formats
• 12/09 Project Presentations

Lecture Handouts

• Class information
• Lecture 1: Introduction, Supplementary notes
• Lecture 2: Marketplace design, In class presentation, Supplementary notes
• Lecture 3: Sponsored search 1, In class presentation
• Lecture 4: Sponsored search 2, In class presentation
• Lecture 5: Display advertising 1, In class presentation
• Lecture 6: Display advertising 2, In class presentation
• Lecture 7: Targeting, In class presentation
• Lecture 8: Recommender systems, In class presentation 1, In class presentation 2
• Lecture 9: Mobile, video, and other emerging formats, In class presentation 1, In class presentation 2

Readings & Other Links

• An interesting video on mobile advertising.
• Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords, B. Edelman, M. Ostrovsky, M. Schwarz.
• Algorithmic Game Theory, Chapter 28 (Sponsored Search Auctions). Online copy linked off Tim Roughgarden's webpage.
• Bidding for Representative Allocations for Display Advertising. Arpita Ghosh, Preston McAfee, Kishore Papineni, Sergei Vassilvitskii.

Assignments

Policy

• Assignments must be done individually. It is an honor code violation to collaborate in any form on assignments.
• Recognizing that students may face unusual circumstances and require some flexibility in the course of the quarter, each student will have a total ofthree free late (calendar) days to use as s/he sees fit. Once these late days are exhausted, any homework turned in late will be penalized 50% per late day.
• All homeworks should be submitted in the slot marked "MS&E 239" in the wooden cabinet near rooms 064 and 036 in the Huang basement.

Assignments

• Assignment 1, Solutions
• Assignment 2, Solutions

Project

Advertising Project
The description of the advertising project is here.

Algorithmic Project
The description of the algorithmic project is here (pdf).

Short Bios

Andrei Broder

• is a Yahoo! Fellow and Vice President for Computational Advertising. Previously he was an IBM Distinguished Engineer and the CTO of the Institute for Search and Text Analysis in IBM Research. From 1999 until 2002 he was Vice President for Research and Chief Scientist at the AltaVista Company. He graduated Summa cum Laude from the Technion, and obtained his M.Sc. and Ph.D. in Computer Science at Stanford, under Don Knuth. His current research interests are centered on computational advertising, web search, context-driven information supply, and randomized algorithms.

• Broder is co-winner of the Best Paper award at WWW6 (for his work on duplicate elimination of web pages) and at WWW9 (for his work on mapping the web). He has authored more than a hundred papers and was awarded thirty patents. He is a member of the National Academy of Engineering, a fellow of ACM and of IEEE, and past chair of the IEEE Technical Committee on Mathematical Foundations of Computing.

Vanja Josifovski

• is Principal Research Scientist and the Lead of the Performance Advertising Group at Yahoo! Research. He joined Yahoo! Research in late 2005 and has since spent most of his time designing and building Yahoo!'s next generation online advertising platforms. As a technical lead, Vanja has contributed to rebuilding Yahoo!'s contextual advertising stack as well as the Sponsored Search Advanced Match platform. He is currently leading a team of researchers and engineers in developing Yahoo!'s next generation targeting platform. His research interest include behavioral targeting, ad selection for sponsored search, content match and graphical advertsing; search engines adaptation for ad selection; data mining and information retrieval techniques for improving ad quality; and click and query log data analysis. Previously, Vanja was a Research Staff Member at the IBM Almaden Research Center working on several projects in database runtime and optimization, federated databases, and enterprise search.

• Vanja has published over 60 peer reviewed publications and has authored over 40 patent applications. He has been a member of the organization and program committees of WWW, WSDM, SIGIR, SIGKDD, VLDB and other major conferences in the information retrieval, search and database areas. He holds a MSc degree from University of Florida and a PhD degree from Linkopings University in Sweden.

Related courses

• CS 276 / LING 286 Information Retrieval and Web Mining (http://www.stanford.edu/class/cs276/)
• MS&E 237: The Social Data Revolution: Data Mining and Electronic Business (to be offered in Spring 2011)

Acknowledgement

We acknowledge gratefully the financial support of the following companies towards student projects:

• Google
• Lateral Sports
• Microsoft
• Yahoo!