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arXiv · 2501.02671

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

Jinkun Han·Wei Li·Yingshu Li·Zhipeng Cai
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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 Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed.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.

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Jinkun Han, Wei Li, Yingshu Li, Zhipeng Cai. 2025-01-05. Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks. https://doi.org/10.1145/3627673.3679564

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