Study on SINA micro-blog personalized recommendation based on semantic network
作者：
Highlights：
• We divided the user interest more carefully.
• The tweets we recommended were from other uncorrelated users.
• The topic extracted by semantic network are more comprehensive and intuitive.
• We considered both tweet heat factor and tweeter authority factor in the recommendation model.
• The recommendation performance can be significantly improved.
摘要
•We divided the user interest more carefully.•The tweets we recommended were from other uncorrelated users.•The topic extracted by semantic network are more comprehensive and intuitive.•We considered both tweet heat factor and tweeter authority factor in the recommendation model.•The recommendation performance can be significantly improved.
论文关键词：Personalized tweets recommendation,Semantic network,K-cores,Factor analysis,Linear regression
论文评审过程：Available online 14 February 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.01.045
