Methods for Fusing Eye Movements and Text Content for Information Retrieval

Methods for Fusing Eye Movements and Text Content for Information Retrieval of the Statistical Machine Learning and Bioinformatics group is a project funded by EU IST Programme, under the PASCAL Network of Excellency.

This project develops new kinds of information retrieval systems, by fusing multimodal implicit relevance feedback data with text content using Bayesian and kernel-based machine learning methods.

A long term goal of information retrieval is to understand the "user's intent". We will study the feasibility of using eye tracking to directly measure the interests at the sentence level, and of coupling the results to other relevant sources to estimate user preferences. The concrete task is to predict relevance for new documents given judgments on old ones. Such predictions can be used in information retrieval, and the most relevant documents can even be proactively offered to the user.

The motivation for this project is that by using eye movements we wish to get rid of part of the tedious ranking of retrieved documents, called relevance feedback in standard information retrieval. Moreover, by using the potentially richer relevance feedback signal we want to access more subtle cues of relevance in addition to the usual binary relevance judgments.

For more information, including the list of publications, see main project page.