METoNR: A meta explanation triplet oriented news recommendation model

2021 ◽  
pp. 107922
Author(s):  
Mingwei Zhang ◽  
Guiping Wang ◽  
Lanlan Ren ◽  
Jianxin Li ◽  
Ke Deng ◽  
...  
Keyword(s):  
2021 ◽  
Author(s):  
Jingkun Wang ◽  
Yipu Chen ◽  
Zichun Wang ◽  
Wen Zhao

2021 ◽  
Author(s):  
Jinsheng Huang ◽  
Zhuobing Han ◽  
Hongyan Xu ◽  
Hongtao Liu
Keyword(s):  

Author(s):  
Victor Lavrenko ◽  
Matt Schmill ◽  
Dawn Lawrie ◽  
Paul Ogilvie ◽  
David Jensen ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhengyou Xia ◽  
Shengwu Xu ◽  
Ningzhong Liu ◽  
Zhengkang Zhao

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.


Author(s):  
M. Jenders ◽  
T. Lindhauer ◽  
G. Kasneci ◽  
R. Krestel ◽  
F. Naumann
Keyword(s):  

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