Recommending twitter users to follow using content and collaborative filtering approaches

By John Hannon, Mike Bennett, Barry Smyth. ACM Conference on Recommender Systems, 2010

509 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e4e60b348fb0a853afc39aa00f3499079028433a

How researchers see this paper

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

In Information Systems

1. Mark Dredze
2. Feng Wang
3. Guanliang Chen
4. Li Chen
5. Derek O'Callaghan
6. Padraig Cunningham
7. Joe Carthy
8. Barry Smyth

In Sociology and Political Science

1. Feng Wang
2. Guanliang Chen
3. Feida Zhu
4. Padraig Cunningham
5. Barry Smyth
6. Ee-Peng Lim
7. Li Chen
8. Qiaozhu Mei

In Statistical and Nonlinear Physics

1. Mark Dredze
2. Feida Zhu
3. Padraig Cunningham
4. Feng Wang
5. Barry Smyth
6. Ee-Peng Lim
7. Li Chen
8. Qiaozhu Mei

Citations in the corpus

• 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.”
• We know what @you #tag: does the dual role affect hashtag adoption? (2012, background): “Finally, our work is also related to the recommender systems in microblogging communities, including the recommendation of followee [16], content [7] and conversations [6] in Twitter.”
• Why I like it: multi-task learning for recommendation and explanation (2018, background): “To improve the accuracy and explainability of recommender systems, the recommender systems community has recently tried to harness user-generated reviews as a new source of recommendation data [4, 9, 10, 12, 13, 18].”
• Characterizing Silent Users in Social Media Communities (2015, background): “While existing research often focuses on recommending content for active users (Hannon, Bennett, and Smyth 2010; Uysal and Croft 2011), it is also important to do the same for lurkers.”
• Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems (2015, methodology): “Following the approach of Hannon, Bennett, and Smyth (2010), a ‘‘profile document’’ was generated for each seed and related channel, consisting of an aggregation of the text metadata from their corresponding uploaded video sample, from which a tokenized vector representation was produced.”
• Recommender systems based on user reviews: the state of the art (2015, background): “The rich opinion information embedded in reviews, such as the multi-faceted nature of feature opinions, contextual opinions, and comparative opinions, makes this branch of research distinguishable from the branches based on simple texts like posts and tweets from social networking websites (Hannon e”