Details
| Original language | English |
|---|---|
| Title of host publication | CHIIR 2020 |
| Subtitle of host publication | Proceedings of the 2020 Conference on Human Information Interaction and Retrieval |
| Pages | 422-426 |
| Number of pages | 5 |
| ISBN (electronic) | 9781450368926 |
| Publication status | Published - 14 Mar 2020 |
| Externally published | Yes |
| Event | CHIIR 2020: ACM SIGIR Conference on Human Information Interaction and Retrieval - Vancouver, Canada Duration: 14 Mar 2020 → 18 Mar 2020 |
Abstract
Throughout the search process, the user's gaze on inspected SERPs and websites can reveal his or her search interests. Gaze behavior can be captured with eye tracking and described with word-eye-fixations. Word-eye-fixations contain the user's accumulated gaze fixation duration on each individual word of a web page. In this work, we analyze the role of word-eye-fixations for predicting query terms. We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms. We use a dataset of 50 search sessions obtained through a lab study in the social sciences domain. Using established machine learning models, we can predict query terms with comparably high accuracy, even with only little training data. Feature analysis shows that the categories Fixation, Query Relevance and Session Topic contain the most effective features for our task.
Keywords
- Eye tracking, Gaze behavior, Prediction, Query terms
ASJC Scopus subject areas
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Information Systems
Cite this
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CHIIR 2020: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. 2020. p. 422-426.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - The Role of Word-Eye-Fixations for Query Term Prediction
AU - Davari, Masoud
AU - Hienert, Daniel
AU - Kern, Dagmar
AU - Dietze, Stefan
PY - 2020/3/14
Y1 - 2020/3/14
N2 - Throughout the search process, the user's gaze on inspected SERPs and websites can reveal his or her search interests. Gaze behavior can be captured with eye tracking and described with word-eye-fixations. Word-eye-fixations contain the user's accumulated gaze fixation duration on each individual word of a web page. In this work, we analyze the role of word-eye-fixations for predicting query terms. We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms. We use a dataset of 50 search sessions obtained through a lab study in the social sciences domain. Using established machine learning models, we can predict query terms with comparably high accuracy, even with only little training data. Feature analysis shows that the categories Fixation, Query Relevance and Session Topic contain the most effective features for our task.
AB - Throughout the search process, the user's gaze on inspected SERPs and websites can reveal his or her search interests. Gaze behavior can be captured with eye tracking and described with word-eye-fixations. Word-eye-fixations contain the user's accumulated gaze fixation duration on each individual word of a web page. In this work, we analyze the role of word-eye-fixations for predicting query terms. We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms. We use a dataset of 50 search sessions obtained through a lab study in the social sciences domain. Using established machine learning models, we can predict query terms with comparably high accuracy, even with only little training data. Feature analysis shows that the categories Fixation, Query Relevance and Session Topic contain the most effective features for our task.
KW - Eye tracking
KW - Gaze behavior
KW - Prediction
KW - Query terms
UR - http://www.scopus.com/inward/record.url?scp=85082443691&partnerID=8YFLogxK
U2 - 10.1145/3343413.3378010
DO - 10.1145/3343413.3378010
M3 - Conference contribution
AN - SCOPUS:85082443691
SP - 422
EP - 426
BT - CHIIR 2020
T2 - CHIIR 2020
Y2 - 14 March 2020 through 18 March 2020
ER -