Neural Network Based Popularity Prediction by Linking Online Content with Knowledge Bases
Predicting the popularity of online items has been an important task to understand and model online popularity dynamics.Featurebased methods are one of the mainstream approaches to tackle this task.However,most of the existing studies focus on some specific kind of auxiliary data,which is usually platform-or domain-dependent.In existing works,the incorporation of auxiliary data has put limits on the applicability of the prediction model itself.These methods may not be applicable to multiple domains or platforms.To address these issues,we propose to link online items with existing knowledge base(KB)entities,and leverage KB information as the context for improving popularity prediction.We represent the KB entity by a latent vector,encoding the related KB information in a compact way.We further propose a novel prediction model based on LSTM networks,adaptively incorporating KB embedding of the target entity and popularity dynamics from items with similar entity information.Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.
Deep learning Popularity prediction Knowledge base
Wayne Xin Zhao Hongjian Dou Yuanpei Zhao Daxiang Dong Ji-Rong Wen
School of Information,Renmin University of China,Beijing,China Baidu Inc.,Beijing,China
国际会议
澳门
英文
16-28
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）