  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.
