用脑电图实时捕捉思维，用量子认知+图网络推荐更准
Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks
- 结合量子认知理论与图卷积网络建模脑电数据
- 在真实脑电数据上推荐准确率显著优于现有模型
- 适合脑机接口、实时个性化推荐研究者
当前推荐系统依赖用户历史行为、社交关系、评分等多模态信息。尽管过时的用户信息能反映兴趣趋势，但无法捕捉用户的实时思维。随着脑机接口的发展，探索能实时反映用户真实想法的下一代推荐系统成为可能。脑电图（EEG）因其便捷性和可移动性，是采集脑信号的有前景方法。然而，由于学习人类脑活动的复杂性，基于脑电的推荐研究仍较少。为探索其应用潜力，本文提出新型神经网络模型QUARK，融合量子认知理论与图卷积网络，实现精准物品推荐。通过大量实验验证，QUARK在性能上优于当前最优推荐模型。
原文摘要 · Abstract (English)
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 Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed.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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