大数据背景下网络定向广告精准传播综合学习模型研究

本项目整合多种学科知识与研究方法，以大数据为背景，对网络定向广告精准传播综合学习模型进行深入研究。首先，基于扎根理论和共词分析等方法，对网络定向广告内涵、特征要素、传播机理等进行分析，从“主体、技术、要素”3个角度，构建“网络定向广告概念动态演化模型”。其次，利用分布式计算、半监督式学习等前沿技术分别构建受众行为定向、广告内容定向、人口统计定向、地域定向以及平台定向5个传播子模型，进而建立“网络定向广告精准传播综合学习模型”。再次，基于采集的实际数据，一方面应用R语言、Python语言、自组织数据挖掘等工具，对模型进行定量化实证分析，另一方面，应用眼动实验技术，就定向广告界面特性对消费者注意的影响进行研究，最终提出网络定向广告精准传播的优化策略。研究成果有利于企业整合和优化配置不同门户网站的有限广告资源，提升网络定向广告传播效率、降低传播成本，为企业推行网络定向广告提供智力和技术服务支持。

With big data as the background, this program will integrate multiple subjects and research methods, to do intensive study on precise spread and comprehensive learning model of targeted online advertising. Firstly, based on grounded theory, analysis of common words, and so on, the program will analyze the content, characteristics, transmission mechanism and others of targeted online advertising; also, from the perspective of "subject, technology, elements", the program will build conceptual dynamic evolution model for targeted online advertising. Secondly, by using cutting-edge technology such as distributed computing and semi-supervised learning, the program will build five sub models: audience behavior orientation, advertising content orientation, demographic orientation, regional orientation and platform orientation, and then establish precise spread and comprehensive learning model of targeted online advertising. Lastly, based on the actual data, on the one hand, using tools such as R language, Python language, self-organizing data mining, the program will carry out quantitative empirical analysis on the model, on the other hand, with the application of eye movement experiment, the program will study the influence of interface characteristics of targeted online advertising on consumers' attention, to put forward the final optimization strategy for targeted online advertising’s precise transmission. The results of this program will help enterprise integrate and optimize different portals online resources, and improve network targeted advertising efficiency, reduce the cost of dissemination, provide scientific evidence and technical support for enterprises, so achieve more precise spread of targeted online advertising and attract more attention of consumers. Not only can the research be expected to help enterprises to choose suitable advertising voices, but also establish a quantitative and standardized system for selecting advertising voice actors.