CiteULike: Group: Adaptive-Web - with tag eye-tracking

Relevance feedback from eye movements for proactive information retrieval

S Arvi — 2008-02-06T18:51:30-00:00

(2004)

Introduction. This on-going work is part of a larger project that aims at complementing or even replacing the laborious explicit relevance feedback in information retrieval by implicit feedback. Technology for measuring eye movements begins to be mature enough, and the movements contain rich information about the attention and interest patterns of the user. The problem is that the signal is very noisy and the correspondence of the eye xation patterns to user's attention is not always...

Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation

Cristina Conati — 2007-11-30T20:02:13-00:00

Knowledge-Based Systems, Vol. 20, No. 6. (August 2007), pp. 557-574.

In this paper, we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning. This work contributes to user modeling and intelligent interfaces research by extending existing research on eye-tracking in HCI to on-line capturing of high-level user mental states for real-time interaction tailoring. We first describe the empirical work we did to understand the user meta-cognitive behaviors to be modeled. We then illustrate the probabilistic user model we designed to capture these behaviors with the help of on-line information on user attention patterns derived from eye-tracking data. Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence. Finally, we discuss work we have done on using pupil dilation information, also gathered through eye-tracking data, to further improve model accuracy.

Combining eye movements and collaborative filtering for proactive information retrieval

Kai Puolamäki — 2007-10-08T18:48:24-00:00

(2005), pp. 146-153.

Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments

Christina Merten — 2007-04-20T19:39:08-00:00

(2006), pp. 39-46.

The determinants of web page viewing behavior: an eye-tracking study

Bing Pan — 2005-10-06T23:46:11-00:00

(2004), pp. 147-154.

Eye tracking in web search tasks: design implications

Joseph Goldberg — 2005-06-14T16:01:43-00:00

(2002), pp. 51-58.

Eye-tracking analysis of user behavior in WWW search

Laura Granka — 2007-02-03T21:54:42-00:00

(2004), pp. 478-479.

Unsupervised and supervised machine learning in user modeling for intelligent learning environments

Saleema Amershi — 2007-02-03T17:49:55-00:00

(2007), pp. 72-81.

The influence of task and gender on search and evaluation behavior using Google

Lori Lorigo — 2007-02-03T17:01:18-00:00

Inf. Process. Manage., Vol. 42, No. 4. (July 2006), pp. 1123-1131.

Accurately interpreting clickthrough data as implicit feedback

Thorsten Joachims — 2005-11-03T14:08:57-00:00

(2005), pp. 154-161.

This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.

Navigation in degree of interest trees

Raluca Budiu — 2006-08-25T12:03:46-00:00

(2006), pp. 457-462.