基于标签卷积神经网络的文本推荐算法

在面向用户的文章收集系统中,用户会将自己喜欢的文章收集起来构成自己的偏好文章集合,理解用户为何喜欢特定文章、如何精确的找到用户喜欢的文章目前成为了一个重要的研究课题.本文通过基于面向用户的文章收集系统中的一些相关信息,比如文本信息、标签等,来辅助推荐系统更好的进行文章的推荐.文中提出了基于标签卷积神经网络的文本推荐算法,结合神经网络和协同过滤算法的同时,将标签加入到神经网络的设计中.通过在真实的citeulike数据集进行的实验和验证,使用本文的模型可以有效的提高对用户偏好文章预测的准确性.

Personalized Academic Article Recommendation with Tagged Convolutional Nets

In user-oriented article collection system, users construct their collection sets by adding articles which they are interested in, studying why users desire specific articles and find the specific articles is particularly an interesting issue in social science. In this paper, we present the prediction users'' preference on articles by considering content information, i.e. semantic information and tags. In this study, we propose a new model which jointly performs deep representation learning for the content information and collaborative filtering for the collection matrix. Extensive experiments on the real-world datasets show that it can significantly advance the state-of-the-art.