Evaluating the Robustness of Learning from Implicit Feedback

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.

• Type: Journal
• Year: 2006
• Where: CORR
• Authors: Filip Radlinski, Thorsten Joachims
• Keywords: CORR 2006, Education, Possible User Behaviors, Real-world Settings, User Behaviors