Tuesday, November 30

Lectures on User Behavior Modeling and Implicit Feedback from Query Logs

I am one of the TAs for the graduate IR course at UMass this semester. I recently gave two lectures on modeling user behavior and utilizing implicit user feedback from logs.

User Behavior Modeling. I covered models of information seeking behavior. Then, I went over the Google 3M (micro-, meso-, and macro-) characterizations of interactions. We looked at how we learn about these various levels of interactions through field and lab studies, instrumented panels, and query logs.

Implicit User Feedback. We finished up query log analysis including query classification, applications like disambiguation and trends. Most of the time was spent on interpreting clickthrough and browsing behavior to generate preference and relevance data.

If you want to learn more, a lot of the lectures build on materials from Eugene Agichtein's tutorial on Inferring User Intent at WWW 2010. If you want more detail, their intent project is a good place to start.

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