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

2024-10-21

International Conference on Information and Knowledge Management

AI Summary

Existing recommender systems heavily rely on behavioral logs and social relations, limiting their ability to capture users’ transient cognitive states. To address this, we propose QUARK—the first end-to-end electroencephalography (EEG)-driven recommendation model. Methodologically, QUARK uniquely integrates quantum cognition theory with graph neural networks to formalize superposition and interference effects inherent in human decision-making; it jointly leverages EEG time-frequency feature extraction, graph convolutional networks (GCNs) for modeling user–item interaction graphs, and a cross-modal alignment mechanism to enable millisecond-level cognitive state decoding and personalized recommendation. Evaluated on a real-world EEG-based recommendation task, QUARK substantially outperforms state-of-the-art methods, achieving a 12.7% improvement in accuracy. This work is the first to empirically validate the feasibility and superiority of direct brain-signal integration in recommender systems, establishing a novel paradigm for cognition-aware recommendation.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Quantum Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed.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.

Problem

• Recommendation System
• User Preference Understanding
• Behavioral Data Limitations

Innovation

• Quantum Cognitive Theory
• Graph Neural Networks
• EEG-based Recommendation System