Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System

TL;DR

This paper introduces BTA, a novel neural network leveraging EEG brain topography to model user satisfaction in search and recommendation systems, significantly enhancing system performance.

Contribution

The paper proposes BTA, a brain topography adaptive network with multi-centrality encoding and spatial attention, for improved satisfaction modeling using EEG signals.

Findings

BTA effectively captures cognitive connectivities in EEG data.

Incorporating brain signals improves system performance.

BTA enhances satisfaction prediction accuracy in real-world tasks.

Abstract

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.