22 citations · 42 across the 2 of their papers we have counts for
5 papers
S-Walk: Accurate and Scalable Session-based Recommendationwith Random Walks
Minjin Choi, Jinhong Kim, Joonsek Lee +2
Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-sess…
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories
Minjin Choi, Sunkyung Lee, Eunseong Choi +4
Automated metaphor detection is a challenging task to identify metaphorical expressions of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models,…
Local Collaborative Autoencoders
Minjin Choi, Yoonki Jeong, Joonseok Lee +1
Top-N recommendation is a challenging problem because complex and sparse user-item interactions should be adequately addressed to achieve high-quality recommendation results. The l…
Session-aware Linear Item-Item Models for Session-based Recommendation
Minjin Choi, jinhong Kim, Joonseok Lee +2
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Spec…
Collaborative Distillation for Top-N Recommendation
Jae-woong Lee, Minjin Choi, Jongwuk Lee +1
Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model. Despite the success of KD in the…