Toward Whole-Session Relevance: Exploring Intrinsic Diversity in Web Search
Monday, May 19, 2014 - 4:00pm - 4:50pm
KEC 1001
Paul Bennett
Senior Researcher
Context, Learning & User Experience for Search (CLUES) group
Microsoft Research
Abstract:
Current research on web search has focused on optimizing and evaluating single
queries. However, a significant fraction of user queries are part of more
complex tasks which span multiple queries across one or more search sessions.
An ideal search engine would not only retrieve relevant results for a user's
particular query but also be able to identify when the user is engaged in a
more complex task and aid the user in completing that task. Toward optimizing
whole-session or task relevance, we characterize and address the problem of
intrinsic diversity (ID) in retrieval, a type of complex task that requires
multiple interactions with current search engines. Unlike existing work on
extrinsic diversity that deals with ambiguity in intent across multiple users,
ID queries often have little ambiguity in intent but seek content covering a
variety of aspects on a shared theme. In such scenarios, the underlying needs
are typically exploratory, comparative, or breadth-oriented in nat!
ure. We identify and address three key problems for ID retrieval: identifying
authentic examples of ID tasks from post-hoc analysis of behavioral signals in
search logs; learning to identify initiator queries that mark the start of an
ID search task; and given an initiator query, predicting which content to
pre-fetch and rank.
This is joint work with Karthik Raman and Kevyn Collins-Thompson and was the
winner of the SIGIR 2013 Best Student Paper Award.
Biography:
Paul Bennett is a Senior 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 La!
fferty.
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