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In cooperation with the Society for Industrial and Applied Mathematics (SIAM)

Description:

E-commerce is pioneering new kinds of advertising and new kinds of customer service. These innovations pose novel challenges in statistics and mathematics. For example:

* A search engine company might write a contract to present an on-line advertisement to 2 million males in California between the ages of 20 and 40 in November, and another contract to present a different ad to 4 million males aged 20 to 60 in Southern California during November and December. The search engine company needs to decide how to divide up the demographic characteristics of the users for sale, how to price different combinations of characteristics, and how the price of a contract should depend upon the time horizon before the service is due (i.e., a demographically specific contract in the distant future may preclude many other advertisment sales, and the price should reflect that; in contrast, a contract for advertising tomorrow is almost pure revenue, provided that the demographic breakouts are available).

* Advertisements bid against each other for keywords. The statistical characteristics of these high-speed auctions is unclear, and there are strategies for handicapping the bidders in order to achieve economically desirable outcomes, or to develop better mechanism design.

* Recommender systems are a key component of search engine advertisement technology. All major recommender systems are proprietary (with a narrow exception for the Netflix competition), but the statistical strategies involved in these kinds of complex data mining problem are an important opportunity in the open research domain.

The mathematical challenges that arise in computational advertising include massive, high-speed linear programming, better agent-based models for auction dynamics, and the computational finance behind dynamic management of the sales portfolio. The statistical challenges include modeling and forecasting of trends among users, prediction methodology for recommender systems, and modeling the revenue streams.

Details:

This two-week program will run from August 6 to August 17, 2012, at the Radisson RTP in Research Triangle Park, NC.  The location is in close proximity to SAMSI. The first three days will be spent on technical presentations by leading researchers and industry experts, to bring everyone up to speed on the currently used methodology. On the fourth day, the participants will self-organize into working groups, each of which will address one of the key problem areas (it is permitted that people join more than one group, and the organizers will try to arrange the working group schedules to faciliate that).

The activities in the working groups will address real-world datasets provided by the corporate participants. Datasets that will be made available include:

Recommender systems data: (1) Netflix competition data; (2) Y! Music data available from KDD cup 2011; (3) Movielens dataset available from GroupLens; (4) Y! front page data (to be released, tentative at this point)

Web Search: (1) Learning to Rank competition data.

Advertising: (1) Click logs from a real-world system (to be released, tentative at this point).

e-commerce:( 1) Epinions: data to study user trust and product ratings.

Program Organizers: Deepak Agarwal (Yahoo!) and Diane Lambert (Google); Local Organizer: David Banks, Duke University; SAMSI Liaison: Ilse Ipsen, SAMSI and North Carolina State University

Participants:

Confirmed and Semi-confirmed participants include:

Deepak Agarwal, Yahoo!
David Banks, Duke University
Robert Bell, AT&T Labs
David Blei, Princeton University
Ming-hui Chen, Google
Diane Lambert, Yahoo!
Art Owen, Stanford University
Jan Pedersen, Microsoft
Christian Posse, LinkedIn
Bonnie Ray, IBM
Neel Sundersan, eBay
Tian Zheng, Columbia University

Please send questions to [email protected]

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Analyticbridge offers free webinars and white papers about computational and viral marketing. Email us at [email protected] for details about our courses, certifications and success stories: the founder, Dr. Vincent Granville (Cambridge University graduate), holds several patents and has 20 years of expertise with automated keyword bidding as well as with auction pricing and quantitative trading, including with clients such as eBay, Wells Fargo and Microsoft, and with major ad networks. Our Chief Architect is regularly an invited speaker at international data mining and text mining conferences.

If you successfully complete our program, you will earn the Chartered Analyst certification. 

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