PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

User models from implicit feedback for proactive information retrieval
Samuel Kaski, Petri Myllymäki and Ilpo Kojo
In: Workshop 4 of the 10th International Conference on User Modeling; Machine Learning for User Modeling: Challenges, Edinburgh, Scotland(2005).

Abstract

Our research consortium develops user modeling methods for proactive applications. In this project we use machine learning methods for predicting users' preferences from implicit relevance feedback. Our prototype application is information retrieval, where the feedback signal is measured from eye movements or user's behavior. Relevance of a read text is extracted from the feedback signal with models learned from a collected data set. Since it is hard to define relevance in general, we have constructed an experimental setting where relevance is known a priori.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
ID Code:1697
Deposited By:Samuel Kaski
Deposited On:28 November 2005