@inproceedings{ca81d4933c2f4751b2b94ebdd32a0c53,
title = "Inferring users{\textquoteright} critiquing feedback on recommendations from eye movements",
abstract = "In recommender systems, critiquing has been popularly applied as an effective approach to obtaining users{\textquoteright} feedback on recommended products. In order to reduce users{\textquoteright} efforts of creating critiquing criteria on their own, some systems have aimed at suggesting critiques for users to choose. How to accurately match system-suggested critiques to users{\textquoteright} intended feedback hence becomes a challenging issue. In this paper, we particularly take into account users{\textquoteright} eye movements on recommendations to infer their critiquing feedback. Based on a collection of real users{\textquoteright} eye-gaze data, we have demonstrated the approach{\textquoteright}s feasibility of implicitly deriving users{\textquoteright} critiquing criteria. It hence indicates a promising direction of using eye-tracking technique to improve existing critique suggestion methods.",
keywords = "Critiquing feedback, Eye movements, Feedback inference, Fixation metrics, Recommender systems",
author = "Li Chen and Feng Wang and Wen Wu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 24th International Conference on Case-Based Reasoning Research and Development, ICCBR 2016 ; Conference date: 31-10-2016 Through 02-11-2016",
year = "2016",
doi = "10.1007/978-3-319-47096-2\_5",
language = "英语",
isbn = "9783319470955",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "62--76",
editor = "\{Belen Diaz-Agudo\}, M. and Thomas Roth-Berghofer and Ashok Goel",
booktitle = "Case-Based Reasoning Research and Development - 24th International Conference, ICCBR 2016, Proceedings",
address = "德国",
}