Recent Yahoo! Research works in data mining for online advertising

As a great part of Yahoo! revenue comes from online advertising, it is not surprising that Yahoo! Research devotes important efforts to optimize their models in order to get more and better clicks to the ads they serve. They have an ongoing project named "Squeeze Every Drop of Meaning from Data", with several goals including: What are the most appropriate advertisements to maximize click-through rates on a particular web page?

Also, they have recently changed their user data retention policy in order to keep the minimum data they need to effectively improve their models. As stated in the news story:

"(They) would retain individual user data for only three months, down from 13 months. Google keeps individualized search data of its users for nine months and Microsoft for 18 months."

Here is a list of recent papers (2008-) from Yahoo! Research dealing with online advertising:

• Online story scheduling in web advertising Dasgupta, A.; Ghosh, A.; Nazerzadeh, H.; Raghavan, P. , SODA 2009, 01/04/2009, (2009)
• Online Learning from Click Data for Sponsored Search Ciaramita, M.; Murdock, V.; Plachouras, V. , The 17th International World Wide Web Conference (WWW), 21/04/2008, Beijing, China, (2008)
• Sharing Online Advertising Revenue with Consumers Chen, Y.; Ghosh, A.; McAfee, P.; Pennock, D. , WINE, 12/16/2008, (2008)
• Ad Auction Design and User Experience Abrams, Z.; Schwarz, M. , Applied Economics Research Bulletin, Special Issue on Theoretical, Empirical, and Experimental Research on Auctions, 03/2008, (2008)
• Semantic Associations for Contextual Advertising Ciaramita, M.; Murdock, V.; Plachouras, V. , Journal of Electronic Commerce Research: Special Issue on Online Advertising and Sponsored Search, 02/2008, Volume 9, Issue 1, p.1-15, (2008)
• Ad Delivery with Budgeted Advertisers: A Comprehensive LP Approach Abrams, Z.; Keerthi, S.S.; Mendelevitch, O.; Tomlin, J.A. , Journal of Electronic Commerce Research, 01/2008, Volume 9, p.16 - 32, (2008)
• An Expressive Auction Design for Online Display Advertising Lahaie, S.; Parkes, D.C.; Pennock, D.M.. , National Conference on Artificial Intelligence (AAAI), (2008)
• A note on search based forecasting of ad volume in contextual Wang, X.; Broder, r.Z.; Fontoura, M.; Josifovski, V. , CIKM, p.1343-1344, (2008)
• Contextual Advertising by Combining Relevance with Click Feedback Chakrabarti, D.; Agarwal, D.; Josifovski, V. , WWW, (2008)
• Externalities in Online Advertising Ghosh, A.; Mahdian, M. , 17th International World Wide Web Conference (WWW), (2008)
• Search advertising using web relevance feedback Broder, r.Z.; Ciccolo, P.; Fontoura, M.; Gabrilovich, E.; Josifovski, V.; Riedel, L. , CIKM, p.1013-1022, (2008)
• Statistical Challenges in Online Advertising Agarwal, D.; Chakrabarti, D. , CIKM 2008, (2008)