22 citations · 70 across the 11 of their papers we have counts for
5 papers · 1 filter
LA-CTR: A Limited Attention Collaborative Topic Regression for Social Media
Jeon-Hyung Kang, Kristina Lerman
Probabilistic models can learn users' preferences from the history of their item adoptions on a social media site, and in turn, recommend new items to users based on learned prefer…
Limited Attention and Centrality in Social Networks
Kristina Lerman, Prachi Jain, Rumi Ghosh +2
How does one find important or influential people in an online social network? Researchers have proposed a variety of centrality measures to identify individuals that are, for exam…
Structural and Cognitive Bottlenecks to Information Access in Social Networks
Jeon-Hyung Kang, Kristina Lerman
Information in networks is non-uniformly distributed, enabling individuals in certain network positions to get preferential access to information. Social scientists have developed…
LA-LDA: A Limited Attention Topic Model for Social Recommendation
Jeon-Hyung Kang, Kristina Lerman, Lise Getoor
Social media users have finite attention which limits the number of incoming messages from friends they can process. Moreover, they pay more attention to opinions and recommendatio…
Transfer Topic Modeling with Ease and Scalability
Jeon-Hyung Kang, Jun Ma, Yan Liu
The increasing volume of short texts generated on social media sites, such as Twitter or Facebook, creates a great demand for effective and efficient topic modeling approaches. Whi…