36 citations · 65 across the 5 of their papers we have counts for
5 papers
Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation
Junsu Cho, Dongmin Hyun, Dong won Lim +3
Sequential Recommender Systems (SRSs) aim to predict the next item that users will consume, by modeling the user interests within their item sequences. While most existing SRSs foc…
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability
Dongmin Hyun, Chanyoung Park, Junsu Cho +1
Sequential recommender systems have shown effective suggestions by capturing users' interest drift. There have been two groups of existing sequential models: user- and item-centric…
Unsupervised Proxy Selection for Session-based Recommender Systems
Junsu Cho, SeongKu Kang, Dongmin Hyun +1
Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i.e., session). Unlike sequence-aware recommen…
Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation
Junsu Cho, Dongmin Hyun, SeongKu Kang +1
Recommender systems have achieved great success in modeling user's preferences on items and predicting the next item the user would consume. Recently, there have been many efforts…
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning
Beomjo Shin, Junsu Cho, Hwanjo Yu +1
Multiple Instance Learning (MIL) involves predicting a single label for a bag of instances, given positive or negative labels at bag-level, without accessing to label for each inst…