基于文本信息处理的深度网络推荐技术研究

网络信息时代,在使用系统的时候,首先应该要避免信息过载所导致的问题。然而这一问题目前已经在网络信息技术中起到了重要的作用。根据传统的模式进行推算,目前互联网想要构建模式,首先应该设计其模型训练,另外其数据不能过于单一,而信息量也不够充足,所以导致目前所出现的推荐效果并不是很好。所以文章中有很多辅助信息能够添加到模型中而获得额外的数据输入,对于文章所推荐的方式,有些用户往往忽视所具备的多样性,主要是由于用户的喜好会随着项目的改变而发生变化。

In the network information age, when using the system, we should first avoid the problems caused by information overload. However, this problem has played an important role in network information technology. According to the traditional model, if the Internet wants to build a model, it should first design its model training;in addition, its data can not be too single,and the amount of information is not sufficient, so the current recommendation effect is not very good. Therefore, in this paper,there are a lot of auxiliary information can be added to the model to obtain additional data input. For the way recommended in this paper, some users often ignore the diversity, mainly because users’ preferences will change with the the project.