Interactive Assessment Tool for Gaze-based Machine Learning Models in Information Retrieval-Reference-Cited by-同舟云学术

Interactive Assessment Tool for Gaze-based Machine Learning Models in Information Retrieval

Published:2022-03-14 Issue: Volume: Page:
ISSN:
Container-title:ACM SIGIR Conference on Human Information Interaction and Retrieval
language:
Short-container-title:

Author:

Valdunciel Pablo¹,Bhatti Omair Shahzad¹,Barz Michael²,Sonntag Daniel²

Affiliation:

1. German Research Center for Artificial Intelligence (DFKI), Germany

2. German Research Center for Artificial Intelligence (DFKI), Germany and Applied Artificial Intelligence, Oldenburg University, Germany

Funder

Bundesministerium für Bildung und Forschung

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3498366.3505834

Reference34 articles.

1. Can eyes reveal interest? Implicit queries from gaze patterns

2. Michael Barz Omair Shahzad Bhatti and Daniel Sonntag. 2021. Implicit Estimation of Paragraph Relevance from Eye Movements. Frontiers in Computer Science(2021). https://doi.org/10.3389/fcomp.2021.808507 10.3389/fcomp.2021.808507

3. Michael Barz Omair Shahzad Bhatti and Daniel Sonntag. 2021. Implicit Estimation of Paragraph Relevance from Eye Movements. Frontiers in Computer Science(2021). https://doi.org/10.3389/fcomp.2021.808507

4. Error-aware gaze-based interfaces for robust mobile gaze interaction

5. Michael Barz , Sven Stauden , and Daniel Sonntag . 2020. Visual Search Target Inference in Natural Interaction Settings with Machine Learning . In ACM Symposium on Eye Tracking Research and Applications(ETRA ’20 Full Papers) , Andreas Bulling, Anke Huckauf, Eakta Jain, Ralph Radach, and Daniel Weiskopf (Eds.). Association for Computing Machinery , 1–8. https://doi.org/10.1145/3379155.3391314 10.1145/3379155.3391314 Michael Barz, Sven Stauden, and Daniel Sonntag. 2020. Visual Search Target Inference in Natural Interaction Settings with Machine Learning. In ACM Symposium on Eye Tracking Research and Applications(ETRA ’20 Full Papers), Andreas Bulling, Anke Huckauf, Eakta Jain, Ralph Radach, and Daniel Weiskopf (Eds.). Association for Computing Machinery, 1–8. https://doi.org/10.1145/3379155.3391314

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