Context-sensitive information retrieval using implicit feedback.
Accurately interpreting clickthrough data as implicit feedback.
Implicit feedback for inferring user preference: A bibliography.
Display time as implicit feedback: Understanding task effects.
Unbiased Offline Recommender Evaluation for Missing-Not-At-Random Implicit Feedback.
Our goal is to develop machine learning algorithms that improve retrieval quality using implicit feedback.
Adaptive search engines use explicit and implicit feedback indicators to model users or search tasks.
Implicit feedback (e.g., clicks, dwell times, etc). is an abundant source of data in human-interactive systems.
Such implicit feedback requires new machine learning methods, since it comes in forms that are different from the standard machine learning settings.
[Joachims/Radlinski/07a] T. Joachims, F. Radlinski, Search Engines that Learn from Implicit Feedback, IEEE Computer, Vol. 40, No. 8, August, 2007.
While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key obstacle to its effective use.
