36 citations · 159 across the 14 of their papers we have counts for
7 papers · 1 filter
MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement
Yunhak Oh, Sukwon Yun, Dongmin Hyun +2
Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new…
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation
Kibum Kim, Dongmin Hyun, Sukwon Yun +1
The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items. While many existing studi…
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…
Out-of-Category Document Identification Using Target-Category Names as Weak Supervision
Dongha Lee, Dongmin Hyun, Jiawei Han +1
Identifying outlier documents, whose content is different from the majority of the documents in a corpus, has played an important role to manage a large text collection. However, d…
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…