Andrei Broder, Google
Short bio: Andrei Broder is a Google Distinguished Scientist. Previously, he was a Fellow and VP for Computational Advertising at Yahoo!. Prior to this, he worked at IBM as a Distinguished Engineer and the CTO of the Institute for Search and Text Analysis and at AltaVista as VP for Research and Chief Scientist. He was graduated Summa cum Laude from Technion, the Israeli Institute of Technology, and obtained his M.Sc. and Ph.D. in Computer Science at Stanford under Don Knuth. Broder has authored more than a hundred papers and was awarded thirty-nine US patents. His current research interests are centered on personalization, computational advertising, web search, context-driven information supply, and randomized algorithms. He is a member of the US National Academy of Engineering, a Fellow of ACM and of IEEE, and co-winner of the 2012 ACM Paris Kanellakis Theory and Practice Award.
Audience Selection in Computational Advertising
Date: Thu, June 13 – 09:00
Abstract: Online interaction is becoming increasingly individualized both via explicit means such as customizations, options, add-ons, skins, apps, etc., and via implicit means, that is, large scale analyses of user behavior (individually and in aggregate) that allow automated, user-specific content and experiences such as individualized top news selection based on inferred interests, personal “radio stations” that capture idiosyncratic tastes from past choices, individually recommended purchases via collaborative filtering, and so on.
Not surprisingly, since online content and services are often ad-funded, online advertising is becoming increasingly user-specific as well, supported by the emerging discipline of Computational Advertising whose main goal is to find the “best match” between a given user in a given context and a suitable ad. There is a wide variety of users and contexts and the number of potential ads 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.
The focus of this talk is audience selection, a form of advertising whereby advertisers specify the features of their desired audience, either explicitly, by specifying characteristics such as demographics, location, and context, or implicitly by providing examples of their ideal audience. A particular form of audience selection is interest-based advertising where the desired audience is characterized by its manifested interests. We will discuss how audience selection fits the optimization framework above, present some of the technical mechanisms for selection, and briefly survey some issues surrounding advertising and user privacy. We will conclude with some speculations about the future of online advertising and pertinent areas of scientific research.
Geert-Jan Houben, Delft University of Technology
Short bio: Full professor of Web Information Systems at the Software Technology department at Delft University of Technology (TU Delft). His main research interests are in Web Engineering, in particular the engineering of Web information systems that involve Web and Semantic Web technology, and User Modeling, Adaptation and Personalization. He is managing editor of JWE, the Journal of Web Engineering, chair of the Steering Committee for ICWE, the International Conference on Web Engineering, and member of the Editorial Board of ACM TWEB, ACM Transactions on the Web.
Link, Like, Follow, Friend:The Social Element in User Modeling and Adaptation
Date: Tue, June 11 – 09:00
Abstract: The social web is having a clear impact in our field of user modeling and adaptation. ‘Links’ and ‘Likes’ as well as ‘Followers’ and ‘Friends’ are part of a large source of data that is generated by users themselves, often for different purposes, and that provides an unprecedented potential for systems to understand their users and to adapt based on that understanding. As we can see from researchers and projects in a number of relevant fields, data on various manifestations of what users do socially on the web brings new opportunities. Exciting ideas are generated and first explorations show promising results. In this talk we take a look back at recent proposals and studies that consider the social web. We determine interesting patterns and we aim to understand the impact on methods and techniques for user modeling and adaptation. At the same time, the social web brings even more challenges. We look forward by identifying challenges that can drive our research. From technical challenges to explore the different social web sources to social challenges to understand how users behave when this potential is unlocked.
Short bio: Dr. Lillian Lee is a professor of computer science at Cornell University. Her research interests include natural language processing, information retrieval, and machine learning. She is the recipient of the inaugural Best Paper Award at HLT-NAACL 2004 (joint with Regina Barzilay), a citation in “Top Picks: Technology Research Advances of 2004” by Technology Research News (also joint with Regina Barzilay), and an Alfred P. Sloan Research Fellowship. Her group’s work has received several mentions in the popular press, including The New York Times, NPR’s All Things Considered, and NBC’s The Today Show.
Date: Wed, June 12 – 09:00
Abstract: As we all know, more and more of life is now manifested online, and many of the digital traces that are left by human activity are increasingly recorded in natural-language format. This availability offers us the opportunity to glean user-modeling information from individual users’ linguistic behaviors. This talk will discuss the particular phenomenon of individual language adaptation, both in the short term and in the longer term. We’ll look at connections between how people adapt their language to particular conversational partners or groups, on the one hand, and on the other hand, those people’s relative power relationships, quality of relationship with the conversational partner, and propensity to remain a part of the group.