Hybrid microblog recommendation with heterogeneous features using deep neural network
作者：
Highlights：
• Hybrid microblog recommendation using deep neural network framework is proposed.
• The two strategies are proposed to extend user interest tags and extract interest topics for obtaining candidate recommended microblogs.
• A group of heterogeneous features are proposed to represent microblogs.
• A deep neural network with multiple hidden layers is designed to rank microblogs.
• Extensive experiment results on two datasets of Twitter and Sina Weibo outperform the state-of-art methods.
摘要
•Hybrid microblog recommendation using deep neural network framework is proposed.•The two strategies are proposed to extend user interest tags and extract interest topics for obtaining candidate recommended microblogs.•A group of heterogeneous features are proposed to represent microblogs.•A deep neural network with multiple hidden layers is designed to rank microblogs.•Extensive experiment results on two datasets of Twitter and Sina Weibo outperform the state-of-art methods.
论文关键词：Hybrid microblog recommendation,Deep neural network,Heterogeneous features,Extended user interest tags,Topic links
论文评审过程：Received 25 March 2020, Revised 23 September 2020, Accepted 28 October 2020, Available online 2 November 2020, Version of Record 10 February 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114191
