针对目前在微博推荐领域主要使用单一向量表示用户兴趣且缺乏对兴趣之间复杂关系的捕捉能力,导致用户兴趣表示不全面,推荐准确性较低的问题,提出了基于双重注意力机制的多兴趣动态路由微博推荐算法(multi-interest network with dynamic...针对目前在微博推荐领域主要使用单一向量表示用户兴趣且缺乏对兴趣之间复杂关系的捕捉能力,导致用户兴趣表示不全面,推荐准确性较低的问题,提出了基于双重注意力机制的多兴趣动态路由微博推荐算法(multi-interest network with dynamic routing microblogging recommendation algorithm based on dual attention mechanism,MINDDouAtt),用于提高用户兴趣的表征能力。首先,通过动态路由从用户行为数据中提取多个兴趣胶囊,并将这些兴趣胶囊输入到自注意力机制中以对不同兴趣胶囊之间的关联信息进行交叉学习,提高兴趣的表征能力。然后,通过引入标签感知注意力机制来调节不同兴趣胶囊之间的重要性,以更好地满足用户的个性化推荐需求。实验表明,MINDDouAtt算法在亚马逊图书、天猫和微博数据集上的S HR@10值相较于最好的对比模型分别提升了33.66%、10.49%、9.60%。该算法能够在电子商务等领域为用户提供更准确和个性化的推荐结果。展开更多
Tea,a globally cultivated crop renowned for its uniqueflavor profile and health-promoting properties,ranks among the most favored functional beverages worldwide.However,diseases severely jeopardize the production and qu...Tea,a globally cultivated crop renowned for its uniqueflavor profile and health-promoting properties,ranks among the most favored functional beverages worldwide.However,diseases severely jeopardize the production and quality of tea leaves,leading to significant economic losses.While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection,manual leaves removal remains time-con-suming and expensive.Utilizing robots for pruning can significantly enhance efficiency and reduce costs.How-ever,the accuracy of object detection directly impacts the overall efficiency of pruning robots.In complex tea plantation environments,complex image backgrounds,the overlapping and occlusion of leaves,as well as small and densely harmful leaves can all introduce interference factors.Existing algorithms perform poorly in detecting small and densely packed targets.To address these challenges,this paper collected a dataset of 1108 images of harmful tea leaves and proposed the YOLO-DBD model.The model excels in efficiently identifying harmful tea leaves with various poses in complex backgrounds,providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning process.The improvements proposed in this study encompass the Cross Stage Partial with Deformable Convolutional Networks v2(C2f-DCN)module,Bi-Level Routing Atten-tion(BRA),Dynamic Head(DyHead),and Focal Complete Intersection over Union(Focal-CIoU)Loss function,enhancing the model’s feature extraction,computation allocation,and perception capabilities.Compared to the baseline model YOLOv8s,mean Average Precision at IoU 0.5(mAP0.5)increased by 6%,and Floating Point Operations Per second(FLOPs)decreased by 3.3 G.展开更多
文摘针对目前在微博推荐领域主要使用单一向量表示用户兴趣且缺乏对兴趣之间复杂关系的捕捉能力,导致用户兴趣表示不全面,推荐准确性较低的问题,提出了基于双重注意力机制的多兴趣动态路由微博推荐算法(multi-interest network with dynamic routing microblogging recommendation algorithm based on dual attention mechanism,MINDDouAtt),用于提高用户兴趣的表征能力。首先,通过动态路由从用户行为数据中提取多个兴趣胶囊,并将这些兴趣胶囊输入到自注意力机制中以对不同兴趣胶囊之间的关联信息进行交叉学习,提高兴趣的表征能力。然后,通过引入标签感知注意力机制来调节不同兴趣胶囊之间的重要性,以更好地满足用户的个性化推荐需求。实验表明,MINDDouAtt算法在亚马逊图书、天猫和微博数据集上的S HR@10值相较于最好的对比模型分别提升了33.66%、10.49%、9.60%。该算法能够在电子商务等领域为用户提供更准确和个性化的推荐结果。
文摘Tea,a globally cultivated crop renowned for its uniqueflavor profile and health-promoting properties,ranks among the most favored functional beverages worldwide.However,diseases severely jeopardize the production and quality of tea leaves,leading to significant economic losses.While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection,manual leaves removal remains time-con-suming and expensive.Utilizing robots for pruning can significantly enhance efficiency and reduce costs.How-ever,the accuracy of object detection directly impacts the overall efficiency of pruning robots.In complex tea plantation environments,complex image backgrounds,the overlapping and occlusion of leaves,as well as small and densely harmful leaves can all introduce interference factors.Existing algorithms perform poorly in detecting small and densely packed targets.To address these challenges,this paper collected a dataset of 1108 images of harmful tea leaves and proposed the YOLO-DBD model.The model excels in efficiently identifying harmful tea leaves with various poses in complex backgrounds,providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning process.The improvements proposed in this study encompass the Cross Stage Partial with Deformable Convolutional Networks v2(C2f-DCN)module,Bi-Level Routing Atten-tion(BRA),Dynamic Head(DyHead),and Focal Complete Intersection over Union(Focal-CIoU)Loss function,enhancing the model’s feature extraction,computation allocation,and perception capabilities.Compared to the baseline model YOLOv8s,mean Average Precision at IoU 0.5(mAP0.5)increased by 6%,and Floating Point Operations Per second(FLOPs)decreased by 3.3 G.
