基于Conv-BiLSTM模型的虚拟社区用户生成内容创新价值识别问题研究:交互协同的视角

【目的】缓解虚拟社区开放式创新用户生成内容信息过载、处理效率低等问题,优化用户生成内容质量,有效识别并分析用户创新内容,提高虚拟社区协同创新绩效。【方法】基于交互协同视角提出一种用户生成内容创新价值识别方法:一是在创新要素特征方面,在用户属性和内容属性的基础上,引入创新要素异质性属性;二是在创新过程特征方面,关注交互内容的时序性和协同性。建立融合要素特征和过程特征的卷积-双向长短时记忆网络(Conv-BiLSTM)模型进行用户生成内容的价值识别。【结果】选取典型虚拟社区数据进行实证研究,结果表明:融合要素特征和过程特征模型的准确率为88.65%,过程特征的引入使模型准确率提升14.22个百分点,协同要素异质性属性的引入使模型准确率提升6.48个百分点,较其他基准模型与组合模型均有不同程度提升。【局限】仅针对虚拟社区创新内容识别取得较好的结果,需要提高模型的泛化能力,进而应用于其他类型协同创新识别方面。【结论】将创新交互的过程特征和协同创新要素异质性属性引入虚拟社区用户生成内容识别模型中,有效提升了识别的准确率,可以为社区开放式创新管理提供技术参考。

Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.