Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter

By Owen Phelan, Kevin McCarthy, Mike Bennett, Barry Smyth. 2011

119 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

How researchers see this paper

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

In Computer Networks and Communications

1. Bin Xu
2. Xing Xie
3. Yingnan Zhu
4. Yongfeng Huang
5. Mark Dredze
6. Miles Osborne
7. Prabhanjan Kambadur
8. Zhenhui Jessie Li

In Information Systems

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

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].”
• How Twitter is Changing the Nature of Financial News Discovery (2016, background): “While researchers have considered tweet recommendation systems [43, 44, 21, 1, 8], it remains a nontrivial challenge to find the right tweet for the right person at the right time.”
• Neural News Recommendation with Multi-Head Self-Attention (2019, background): “Massive news articles are generated everyday and it is impossible for users to read all news to find their interested content (Phelan et al., 2011).”
• Personalized News Recommendation Using Twitter (2013, background): “In [8, 9], they extended their work by considering the public-rank and the friend’s-rank strategy rather than just considering the articles from the users’ index.”
• A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects (2013, background): “In [62], [63], Phelan et al. proposed a recommendation system Buzzer, which uses a content-based approach to rank RSS news by mining trending terms from public or private Twitter timelines.”
• NPA: Neural News Recommendation with Personalized Attention (2019, background): “mber of users to read digital news [7, 18]. However, massive news articles are emerged everyday, and it is impractical for users to seek for interested news from a huge volume of online news articles [26, 34]. Therefore, it is an important task for online news platforms to target user interests and ”
• Neural News Recommendation with Attentive Multi-View Learning (2019, background): “Tens of thousands of news are generated everyday, and it is impossible for users to read all these news articles due to time limit [Phelan et al. , 2011].”
• From chatter to headlines: harnessing the real-time web for personalized news recommendation (2012, unknown): “[21] is a content-based approach that uses tweets to rank news using tf·idf.”