基于量子认知启发的脑电图推荐：一种图神经网络方法
Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks
AI总结：
本文提出QUARK模型，将量子认知理论与图卷积网络结合用于基于脑电图的实时推荐，实验证明其优于现有最先进推荐模型。
摘要：
当前的推荐系统通过考虑用户的历史行为、社会关系、评分以及其他多模态信息来推荐商品。尽管过时的用户信息能够呈现用户兴趣的趋势，但没有任何推荐系统能够真正了解用户的实时想法。随着脑机接口的发展，是时候探索能够无延迟地展示用户实时想法的下一代推荐系统了。脑电图（EEG）因其便捷性和可移动性，是一种有前景的脑信号采集方法。目前，由于学习人类大脑活动的复杂性，基于EEG的推荐研究还很少。为了探索基于EEG推荐的效用，我们提出了一种新颖的神经网络模型QUARK，该模型结合了量子认知理论和图卷积网络，以实现准确的物品推荐。与最先进的推荐模型相比，QUARK的优越性通过大量实验得到了证实。
英文摘要：
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 Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed.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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
