As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in ...As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in intention recognition,this paper designs an air target intention recognition method(KGTLIR)based on Knowledge Graph and Deep Learning.Firstly,the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism.Meanwhile,the accuracy,recall,and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification.After that,an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target.Finally,the results of the two models are fused by evidence theory to obtain the target’s operational intention.Experiments show that the intention recognition accuracy of the KGTLIRmodel can reach 98.48%,which is not only better than most of the air target intention recognition methods,but also demonstrates better interpretability and trustworthiness.展开更多
In the recommendation system,bundle recommendation is a prevalent sales strategy in which a combination of diverse,related,or complementary products is suggested to consumers.Recent methodologies frequently utilize gr...In the recommendation system,bundle recommendation is a prevalent sales strategy in which a combination of diverse,related,or complementary products is suggested to consumers.Recent methodologies frequently utilize graph neural networks to capture information from user-bundle,user-item,and bundle-item interactions,deriving corresponding feature representations.However,these approaches often emphasize the distinctions among these three interaction types or treat them uniformly,neglecting the varying importance within one type of interaction and failing to consider the acquisition of information at varying granularities from different types of interactions.In this study,we employ a graph attention mechanism to process user-bundle interaction information,and optimize it using an association enhancement method to extract and construct coarse-grained information representations for users and bundles.By analyzing interactions between users and items,as well as between bundles and items,we identify disparities in item popularity and update the items’feature representations,facilitating the acquisition of fine-grained information representations for users and bundles.By merging this information,we achieve more comprehensive representations of user intent and bundle characteristics.Extensive experiments on two real-world datasets convincingly demonstrate that our approach significantly advances the task of bundle recommendation,outperforming state-of-the-art methods.展开更多
基金funded by the Project of the National Natural Science Foundation of China,Grant Number 72071209.
文摘As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in intention recognition,this paper designs an air target intention recognition method(KGTLIR)based on Knowledge Graph and Deep Learning.Firstly,the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism.Meanwhile,the accuracy,recall,and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification.After that,an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target.Finally,the results of the two models are fused by evidence theory to obtain the target’s operational intention.Experiments show that the intention recognition accuracy of the KGTLIRmodel can reach 98.48%,which is not only better than most of the air target intention recognition methods,but also demonstrates better interpretability and trustworthiness.
基金supported by the Open Project of Xiangjiang Laboratory(No.23XJ03006)the Open Project of Anhui Provincial Key Laboratory of Multimodal Cognitive Computation,Anhui University(No.MMC202408)+3 种基金the Fundamental Research Funds for the Central Universities,JLU(No.93K172024K17)the Fundamental Research Funds for the Central Universities(No.21623402)the Science and Technology Program of Guangzhou,China(No.2024A04j6317)the Hubei Key Laboratory of Intelligent Robot(Wuhan Institute of Technology)(No.HBIR 202302).
文摘In the recommendation system,bundle recommendation is a prevalent sales strategy in which a combination of diverse,related,or complementary products is suggested to consumers.Recent methodologies frequently utilize graph neural networks to capture information from user-bundle,user-item,and bundle-item interactions,deriving corresponding feature representations.However,these approaches often emphasize the distinctions among these three interaction types or treat them uniformly,neglecting the varying importance within one type of interaction and failing to consider the acquisition of information at varying granularities from different types of interactions.In this study,we employ a graph attention mechanism to process user-bundle interaction information,and optimize it using an association enhancement method to extract and construct coarse-grained information representations for users and bundles.By analyzing interactions between users and items,as well as between bundles and items,we identify disparities in item popularity and update the items’feature representations,facilitating the acquisition of fine-grained information representations for users and bundles.By merging this information,we achieve more comprehensive representations of user intent and bundle characteristics.Extensive experiments on two real-world datasets convincingly demonstrate that our approach significantly advances the task of bundle recommendation,outperforming state-of-the-art methods.
