With the growth of information on the Web, most users heavily rely on information access systems (e.g., search engines, recommender systems, etc.) in their daily lives. During this procedure, modeling users' satisfaction status plays an essential part in improving their experiences with the systems. In this paper, we aim to explore the benefits of using Electroencephalography (EEG) signals for satisfaction modeling in interactive information access system design. Different from existing EEG classification tasks, the arisen of satisfaction involves multiple brain functions, such as arousal, prototypicality, and appraisals, which are related to different brain topographical areas. Thus modeling user satisfaction raises great challenges to existing solutions. To address this challenge, we propose BTA, a Brain Topography Adaptive network with a multi-centrality encoding module and a spatial attention mechanism module to capture cognitive connectivities in different spatial distances. We explore the effectiveness of BTA for satisfaction modeling in two popular information access scenarios, i.e., search and recommendation. Extensive experiments on two real-world datasets verify the effectiveness of introducing brain topography adaptive strategy in satisfaction modeling. Furthermore, we also conduct search result re-ranking task and video rating prediction task based on the satisfaction inferred from brain signals on search and recommendation scenarios, respectively. Experimental results show that brain signals extracted with BTA help improve the performance of interactive information access systems significantly.
Interactive Recommendation Agent with Active User Commands
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China（中国人民大学人工智能学院）；University of Chinese Academy of Sciences, China（中国科学院大学）；Alibaba Group, Beijing, China（阿里巴巴集团）
Classification of User Satisfaction in HRI with Social Signals in the Wild
TH Köln - University of Applied Sciences, Cologne Cobots Lab, Betzdorfer Str. 2, 50679, Germany（TH Köln应用科学大学，科隆Cobots实验室）；KI Park e.V. & FAU - Friedrich-Alexander University of Erlangen-Nuremberg（KI Park协会与弗赖堡-埃尔兰根-纽伦堡大学）
Design Principles Developed through User-Centered and Socio-Technical Methods Improve Clinician Satisfaction, Speed, and Confidence in Pharmacogenomic Clinical Decision Support
Collaboration with Conversational AI Assistants for UX Evaluation: Questions and How to Ask them (Voice vs. Text)
Rochester Institute of Technology（罗切斯特理工学院）；University of Waterloo（滑铁卢大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
