From chatter to headlines: harnessing the real-time web for personalized news recommendation

By Gianmarco De Francisci Morales, Aristides Gionis, Claudio Lucchese. 2012

162 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

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. Anshumali Shrivastava
6. Ping Li
7. Fuzheng Zhang
8. Mingxiao An

In Information Systems

1. Xing Xie
2. Fangzhao Wu
3. Chuhan Wu
4. Yongfeng Huang
5. Fuzheng Zhang
6. Anshumali Shrivastava
7. Mingxiao An
8. Michael Jugovac

In Statistical and Nonlinear Physics

1. Xing Xie
2. Ping Li
3. Dietmar Jannach
4. Xiting Wang
5. Nicholas Jing Yuan
6. Fuzheng Zhang
7. Anshumali Shrivastava

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “To combine the advantages of the former two groups of methods, hybrid methods [12, 24, 25] are further proposed to improve the user profile modeling.”
• Explainable Recommendation through Attentive Multi-View Learning (2019, methodology): “Many methods have been proposed to improve recommendation accuracy, including content-based (Kompan and Bielikov ´ a 2010), collaborative filtering-based (Das et al. 2007) and hybrid methods (De Francisci Morales, Gionis, and Lucchese 2012).”
• News recommender systems - Survey and roads ahead (2018, background): “[133] propose to use information from social network sites to deal with the sparsity problem.”
• A new space for comparing graphs (2014, background): “The peculiarity in the collaboration network gets reflected in the ego network as well.”
• 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 online 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].”
• Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, background): “Acknowledging the potential of personalizing via similar or related users, the focus moved towards exploiting information from user’s social networks (Guy et al., 2010; De Fran-cisci Morales et al., 2012; Guy et al., 2009).”