“Mining, Understanding, and Modeling Context in Web Search”
By Paul Bennett
Information retrieval has made significant progress in returning relevant results for a single query. However, much search activity is conducted within a much richer context of a current task focus, recent search activities as well as longer-term preferences. For example, our ability to accurately interpret the current query can be informed by knowledge of the web pages a searcher was viewing when initiating the search or recent actions of the searcher such as queries issued, results clicked, and pages viewed. We develop a framework that enables representation of a broad variety of context including the searcher's long-term interests, recent activity, current focus, and other user characteristics. We then demonstrate how that can be used to improve the quality of search results. We describe recent progress on three key challenges in this domain: enriching information retrieval via automatically generated metadata; mining contextual signals from large scale logs; and understanding and modeling the combination of short-term and long-term user search behavior.
This talk will present joint work with Nam Nguyen, Krysta Svore, Filip Radlinski, Ryen White, Kevyn Collins-Thompson, Wei Chu, Susan Dumais, Peter Bailey, Emine Yilmaz, Fedor Borisyuk, and Xiaoyuan Cui.
Paul Bennett is a Researcher in the Context, Learning & User Experience for Search (CLUES) group at Microsoft Research where he focuses on the development, improvement, and analysis of machine learning and data mining methods as components of real-world, large-scale adaptive systems. His research has advanced techniques for ensemble methods and the combination of information sources, calibration, consensus methods for noisy supervision labels, active learning and evaluation, supervised classification (with an emphasis on hierarchical classification) and ranking with applications to information retrieval, crowdsourcing, behavioral modeling and analysis, and personalization. His recent work has been recognized with a SIGIR 2012 Best Paper Honorable Mention and a SIGIR 2013 Best Student Paper award. He completed his dissertation on combining text classifiers using reliability indicators in 2006 at Carnegie Mellon where he was advised by Profs. Jaime Carbonell and John Lafferty.