13 citations · 16 across the 4 of their papers we have counts for
5 papers
Improving Product Search Relevance with EAR-MP: A Solution for the CIKM 2025 AnalytiCup
JaeEun Lim, Soomin Kim, Jaeyong Seo +3
Multilingual e-commerce search is challenging due to linguistic diversity and the noise inherent in user-generated queries. This paper documents the solution employed by our team (…
Toward a Better Understanding of Loss Functions for Collaborative Filtering
Seongmin Park, Mincheol Yoon, Jae-woong Lee +2
Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, lo…
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering
Jae-woong Lee, Seongmin Park, Mincheol Yoon +1
Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previo…
Bilateral Self-unbiased Learning from Biased Implicit Feedback
Jae-woong Lee, Seongmin Park, Joonseok Lee +1
Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevan…
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…