Adapted Transformer Network for News Recommendation

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

Popularity-Enhanced News Recommendation with Multi-View Interest Representation

2021 ◽  
Author(s):  
Jingkun Wang ◽  
Yipu Chen ◽  
Zichun Wang ◽  
Wen Zhao
Keyword(s):  
Interest Representation ◽  
News Recommendation

scholarly journals Language models for financial news recommendation

2000 ◽  
Author(s):  
Victor Lavrenko ◽  
Matt Schmill ◽  
Dawn Lawrie ◽  
Paul Ogilvie ◽  
David Jensen ◽  
...  
Keyword(s):  
Language Models ◽  
Financial News ◽  
News Recommendation

scholarly journals Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhengyou Xia ◽  
Shengwu Xu ◽  
Ningzhong Liu ◽  
Zhengkang Zhao
Keyword(s):  
Bayesian Model ◽  
Recommendation System ◽  
Research Work ◽  
Joint Probability ◽  
Real Data ◽  
Online News ◽  
City Government ◽  
Data Sets ◽  
News Websites ◽  
News Recommendation

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.

A Serendipity Model for News Recommendation

2015 ◽  
pp. 111-123 ◽  
Author(s):  
M. Jenders ◽  
T. Lindhauer ◽  
G. Kasneci ◽  
R. Krestel ◽  
F. Naumann
Keyword(s):  
News Recommendation

Personalized news recommendation based on an improved conditional restricted Boltzmann machine

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Linxia Zhong ◽  
Wei Wei ◽  
Shixuan Li
Keyword(s):  
Topic Model ◽  
Restricted Boltzmann Machine ◽  
Boltzmann Machine ◽  
Rating Data ◽  
Content Type ◽  
News Stories ◽  
Highest Weight ◽  
User Coverage ◽  
News Recommendation ◽  
Behaviour Data

Purpose Because of the extensive user coverage of news sites and apps, greater social and commercial value can be realized if users can access their favourite news as easily as possible. However, news has a timeliness factor; there are serious cold start and data sparsity in news recommendation, and news users are more susceptible to recent topical news. Therefore, this study aims to propose a personalized news recommendation approach based on topic model and restricted Boltzmann machine (RBM). Design/methodology/approach Firstly, the model extracts the news topic information based on the LDA2vec topic model. Then, the implicit behaviour data are analysed and converted into explicit rating data according to the rules. The highest weight is assigned to recent hot news stories. Finally, the topic information and the rating data are regarded as the conditional layer and visual layer of the conditional RBM (CRBM) model, respectively, to implement news recommendations. Findings The experimental results show that using LDA2vec-based news topic as a conditional layer in the CRBM model provides a higher prediction rating and improves the effectiveness of news recommendations. Originality/value This study proposes a personalized news recommendation approach based on an improved CRBM. Topic model is applied to news topic extraction and used as the conditional layer of the CRBM. It not only alleviates the sparseness of rating data to improve the efficient in CRBM but also considers that readers are more susceptible to popular or trending news.

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