Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks

TL;DR

This paper introduces QUARK, a novel EEG-based recommendation model that combines Quantum Cognition Theory and Graph Neural Networks to capture real-time user thoughts, outperforming existing models.

Contribution

The paper presents a new neural network model integrating quantum cognition and graph neural networks for EEG-based recommendations, addressing real-time user thought detection.

Findings

QUARK outperforms state-of-the-art recommendation models in experiments.

The model effectively captures real-time user thoughts from EEG signals.

Quantum cognition enhances the interpretability of EEG-based recommendations.

Abstract

Current recommendation systems recommend goods by considering users' historical behaviors, social relations, ratings, and other multi-modals. Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed. With the development of brain-computer interfaces, it is time to explore next-generation recommenders that show users' real-time thoughts without delay. Electroencephalography (EEG) is a promising method of collecting brain signals because of its convenience and mobility. Currently, there is only few research on EEG-based recommendations due to the complexity of learning human brain activity. To explore the utility of EEG-based recommendation, we propose a novel neural network model, QUARK, combining Quantum Cognition Theory and Graph Convolutional Networks for accurate item recommendations. Compared with the state-of-the-art recommendation models, the superiority of QUARK is confirmed via extensive experiments.