随着会话推荐的广泛应用,如何充分利用语义信息、建模用户跨会话兴趣以及抑制数据噪声成为提升推荐性能的关键。为此提出一种新颖的会话推荐增强框架LGSBR,通过整合大语言模型(large language model,LLM)的语义理解能力与图神经网络(gra...随着会话推荐的广泛应用,如何充分利用语义信息、建模用户跨会话兴趣以及抑制数据噪声成为提升推荐性能的关键。为此提出一种新颖的会话推荐增强框架LGSBR,通过整合大语言模型(large language model,LLM)的语义理解能力与图神经网络(graph neural network,GNN)的结构建模能力,实现语义增强与个性化推荐。具体而言,利用大语言模型及微调的语言模型生成项目补充文本嵌入和用户跨会话兴趣嵌入,通过软注意力机制融合文本与ID嵌入,生成语义丰富的项目表示;引入用户兴趣嵌入,结合对齐损失实现个性化推荐;最后通过两阶段权重学习过滤噪声项目,优化会话表示。实验结果表明,在Beauty数据集上,LGSBR的P@20达到21.38%,MRR@20达到6.76%,分别较SR-GNN基线提升23.3%和50.56%;在MovieLen-1M数据集上,P@20为25.86%,MRR@20为7.58%,分别提升12.63%和10.98%;研究验证了LGSBR在多种GNN模型上的通用性和有效性。展开更多
We study a mathematical model of biological neuronal networks composed by any finite number N ≥ 2 of non-necessarily identical cells. The model is a deterministic dynamical system governed by finite-dimensional impul...We study a mathematical model of biological neuronal networks composed by any finite number N ≥ 2 of non-necessarily identical cells. The model is a deterministic dynamical system governed by finite-dimensional impulsive differential equations. The statical structure of the network is described by a directed and weighted graph whose nodes are certain subsets of neurons, and whose edges are the groups of synaptical connections among those subsets. First, we prove that among all the possible networks such as their respective graphs are mutually isomorphic, there exists a dynamical optimum. This optimal network exhibits the richest dynamics: namely, it is capable to show the most diverse set of responses (i.e. orbits in the future) under external stimulus or signals. Second, we prove that all the neurons of a dynamically optimal neuronal network necessarily satisfy Dale’s Principle, i.e. each neuron must be either excitatory or inhibitory, but not mixed. So, Dale’s Principle is a mathematical necessary consequence of a theoretic optimization process of the dynamics of the network. Finally, we prove that Dale’s Principle is not sufficient for the dynamical optimization of the network.展开更多
阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood...阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood AgGrEgation,GraphNAGE)的AD分类新方法.首先进行图数据建模,将AD数据样本表示为图数据.采用基于互信息(Mutual Information,MI)的特征选择方法,从样本的114维大脑皮层与皮层下感兴趣区域(Cerebral Cortex and Subcortical Regions Of Interest,CCS-ROI)的体积特征中选取重要性高的体积特征,并将其用于节点建模.提出基于相似性度量的关系建模方法,利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE,针对每个节点,基于与该节点相关的边的权重进行最近邻域采样,然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合,最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE(The Alzheimer’s Disease Prediction Of Longitudinal Evolution)数据集上进行实验,结果表明:本文提出的AD分类方法的准确率(ACCuracy,ACC)为98.20%,F_(1)分数为97.34%,曲线下面积(Area Under Curve,AUC)为97.80%.实验结果表明:本文提出的AD分类方法充分利用了AD数据样本之间的相关性,其性能优于传统的基于机器学习、深度学习和图神经网络(Graph Neural Network,GNN)的AD分类方法.展开更多
文摘随着会话推荐的广泛应用,如何充分利用语义信息、建模用户跨会话兴趣以及抑制数据噪声成为提升推荐性能的关键。为此提出一种新颖的会话推荐增强框架LGSBR,通过整合大语言模型(large language model,LLM)的语义理解能力与图神经网络(graph neural network,GNN)的结构建模能力,实现语义增强与个性化推荐。具体而言,利用大语言模型及微调的语言模型生成项目补充文本嵌入和用户跨会话兴趣嵌入,通过软注意力机制融合文本与ID嵌入,生成语义丰富的项目表示;引入用户兴趣嵌入,结合对齐损失实现个性化推荐;最后通过两阶段权重学习过滤噪声项目,优化会话表示。实验结果表明,在Beauty数据集上,LGSBR的P@20达到21.38%,MRR@20达到6.76%,分别较SR-GNN基线提升23.3%和50.56%;在MovieLen-1M数据集上,P@20为25.86%,MRR@20为7.58%,分别提升12.63%和10.98%;研究验证了LGSBR在多种GNN模型上的通用性和有效性。
文摘We study a mathematical model of biological neuronal networks composed by any finite number N ≥ 2 of non-necessarily identical cells. The model is a deterministic dynamical system governed by finite-dimensional impulsive differential equations. The statical structure of the network is described by a directed and weighted graph whose nodes are certain subsets of neurons, and whose edges are the groups of synaptical connections among those subsets. First, we prove that among all the possible networks such as their respective graphs are mutually isomorphic, there exists a dynamical optimum. This optimal network exhibits the richest dynamics: namely, it is capable to show the most diverse set of responses (i.e. orbits in the future) under external stimulus or signals. Second, we prove that all the neurons of a dynamically optimal neuronal network necessarily satisfy Dale’s Principle, i.e. each neuron must be either excitatory or inhibitory, but not mixed. So, Dale’s Principle is a mathematical necessary consequence of a theoretic optimization process of the dynamics of the network. Finally, we prove that Dale’s Principle is not sufficient for the dynamical optimization of the network.
文摘阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood AgGrEgation,GraphNAGE)的AD分类新方法.首先进行图数据建模,将AD数据样本表示为图数据.采用基于互信息(Mutual Information,MI)的特征选择方法,从样本的114维大脑皮层与皮层下感兴趣区域(Cerebral Cortex and Subcortical Regions Of Interest,CCS-ROI)的体积特征中选取重要性高的体积特征,并将其用于节点建模.提出基于相似性度量的关系建模方法,利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE,针对每个节点,基于与该节点相关的边的权重进行最近邻域采样,然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合,最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE(The Alzheimer’s Disease Prediction Of Longitudinal Evolution)数据集上进行实验,结果表明:本文提出的AD分类方法的准确率(ACCuracy,ACC)为98.20%,F_(1)分数为97.34%,曲线下面积(Area Under Curve,AUC)为97.80%.实验结果表明:本文提出的AD分类方法充分利用了AD数据样本之间的相关性,其性能优于传统的基于机器学习、深度学习和图神经网络(Graph Neural Network,GNN)的AD分类方法.
