D
Donghyuk Shin
Researcher at University of Texas at Austin
Publications - 21
Citations - 519
Donghyuk Shin is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Social media & Statistical classification. The author has an hindex of 10, co-authored 18 publications receiving 449 citations. Previous affiliations of Donghyuk Shin include Arizona State University & Sogang University.
Papers
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Journal ArticleDOI
Block-based noise estimation using adaptive Gaussian filtering
TL;DR: A fast noise estimation algorithm using a Gaussian pre-filter that can be applied to noise reduction in commercial image- or video-based applications such as digital cameras and digital television (DTV) for its performance and simplicity.
Proceedings ArticleDOI
Which app will you use next?: collaborative filtering with interactional context
TL;DR: This paper proposes a methodname algorithm that works in two stages, where users are clustered by their transition behavior, and cluster-level Markov models are computed, and personalized PageRank is computed for a given user on the corresponding cluster Markov graph, with a personalization vector derived from the current context.
Proceedings ArticleDOI
Tumblr Blog Recommendation with Boosted Inductive Matrix Completion
TL;DR: A novel boosted inductive matrix completion method (BIMC) for blog recommendation using an additive low-rank model for user-blog preferences consisting of two components; one component captures the low- rank structure of follow relationships and the other captures the latent structure using side-information.
Journal ArticleDOI
Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach
TL;DR: A visual data analytics framework to enhance social media research using deep learning models including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content is proposed.
Proceedings ArticleDOI
Multi-scale link prediction
TL;DR: Multi-Scale Link Prediction (MSLP) is proposed, a framework for link prediction, which can handle massive networks and construct low-rank approximations of the network at multiple scales in an efficient manner using a fast tree-structured approximation algorithm.