  This paper evaluates the robustness of learning from implicit feedback in web
search. In particular, we create a model of user behavior by drawing upon user
studies in laboratory and real-world settings. The model is used to understand
the effect of user behavior on the performance of a learning algorithm for
ranked retrieval. We explore a wide range of possible user behaviors and find
that learning from implicit feedback can be surprisingly robust. This
complements previous results that demonstrated our algorithm's effectiveness in
a real-world search engine application.
