Make new friends, but keep the old: recommending people on social networking sites
By Jilin Chen, Werner Geyer, Casey Dugan, Michael J. Muller, Ido Guy. International Conference on Human Factors in Computing Systems, 2009
524 citations recorded by the source corpus.
Subfields: Information Systems, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/9d18027bd5ec2bfbdd3d1c3dacca843b950ae453
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
- Michael D. Ekstrand
- John Riedl
- J. Konstan
- Yu Zheng
- Barry Smyth
- Charu C. Aggarwal
- Jie Bao
- Michael S. Bernstein
In Statistical and Nonlinear Physics
- Jie Bao
- David Wilkie
- Barry Smyth
- Michael S. Bernstein
- Mohamed F. Mokbel
- Ed H. Chi
- Yu Zheng
- J. Konstan
Citations in the corpus
- Recommending twitter users to follow using content and collaborative filtering approaches (2010, background): “With this in mind we develop a followee recommender for Twitter; see also [2].”
- Social Media Use in Organizations: Exploring the Affordances of Visibility, Editability, Persistence, and Association (2013, unknown): “…researchers found that implementation of four different friend recommender systems in the SNS at IBM all expanded friend networks in different ways (Chen, Geyer, Dugan, Muller, & Guy, 2009), and concluded that organizations might want to try different algorithms to support connections in order to…”
- Short and tweet: experiments on recommending content from information streams (2010, methodology): “Ranking URLs Using Topic Relevance Using topic relevance is an established approach to compute recommendations [4, 5, 7, 12, 13, 14].”
- Collaborative Filtering Recommender Systems (2011, background): “Users’ social network information was more useful for finding people they already knew on the system, but recommender algorithms based on similarity in user profile contents were more effective at helping users discover new friends [28].”
- Recommendations in location-based social networks: a survey (2015, background): “User recommendations, which includes popular user discovery [12, 38, 98], friend recommendation [5, 16, 82, 104, 117], and community discovery [60, 103], have been extensively studied in the context of traditional social networks.”