Ontology-based news recommendation

By Wouter IJntema, Frank Goossen, Flavius Frasincar, Frederik Hogenboom. 2010

192 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xing Xie
2. Fangzhao Wu
3. Chuhan Wu
4. Yongfeng Huang
5. Tao Qi
6. Fuzheng Zhang
7. Mingxiao An
8. Jianqiang Huang

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “The current methods can be generally categorized as content based methods [19, 22, 33], collaborative filtering based methods [11, 28, 34], and hybrid methods [12, 24, 25].”
• Neural News Recommendation with Multi-Head Self-Attention (2019, background): “Thus, personalized news recommendation is very important for online news platforms to target user interests and alleviate information overload (IJntema et al., 2010).”
• Offline and online evaluation of news recommender systems at swissinfo.ch (2014, background): “There exist many news recommender systems [1, 2, 3, 10, 14], however we decided to select only these baselines for”
• Personalized News Recommendation Using Twitter (2013, methodology): “Wouter et al. also describe ontology-based methods to recommend news articles to the users depending on their interests [5].”
• News recommender systems - Survey and roads ahead (2018, methodology): “[74] 2010 CB User Profiling Athena Accuracy, Precision, Recall, Specificity User Study”
• Neural News Recommendation with Attentive Multi-View Learning (2019, background): “Therefore, personalized news recommendation is very important for online news platforms to help users find their interested news and alleviate information overload [IJntema et al. , 2010; De Francisci Morales et al. , 2012 ] .”
• NPA: Neural News Recommendation with Personalized Attention (2019, background): “Therefore, it is an important task for on-line news platforms to target user interests and make personalized news recommendation [1, 8, 14], which can help users to find their interested news articles and alleviate information overload [32, 35].”