Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis
Abstract
Two general challenges faced by data analysis are the existence of noise and the extraction of meaningful information from collected data. In this study, we used a multiscale framework to reduce the effects caused by noise and to extract explainable geometric properties to characterize finite metric spaces. We conducted lab experiments that integrated the use of eye‐tracking, electrodermal activity (EDA), and user logs to explore users' information‐seeking behaviors on search engine result pages (SERPs). Experimental results of 1,590 search queries showed that the proposed strategies effectively predicted query‐level user satisfaction using EDA and eye‐tracking data. The bootstrap analysis showed that combining EDA and eye‐tracking data with user behavior data extracted from user logs led to a significantly better linear model fit than using user behavior data alone. Furthermore, cross‐user and cross‐task validations showed that our methods can be generalized to different search engine users performing different preassigned tasks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 28, 2019
- Source ID
- 10.1002/asi.24240
Entities
People
- Shing‐tung Yau
- Yen‐hsi Richard Tsai
- Yingying Wu
- Yiqun Liu
Organizations
- Air Force Office of Scientific Research
- Harvard University
- Korea National Institute of Health
- National Natural Science Foundation of China
- Tsinghua University
- University of Texas at Austin