11 citations · 27 across the 7 of their papers we have counts for
7 papers
Rectifying Demonstration Shortcut in In-Context Learning
Joonwon Jang, Sanghwan Jang, Wonbin Kweon +2
Large language models (LLMs) are able to solve various tasks with only a few demonstrations utilizing their in-context learning (ICL) abilities. However, LLMs often rely on their p…
Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
Wonbin Kweon, Hwanjo Yu
Recommender systems often suffer from selection bias as users tend to rate their preferred items. The datasets collected under such conditions exhibit entries missing not at random…
Deep Rating Elicitation for New Users in Collaborative Filtering
Wonbin Kweon, SeongKu Kang, Junyoung Hwang +1
Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial r…
Confidence Calibration for Recommender Systems and Its Applications
Wonbin Kweon
Despite the importance of having a measure of confidence in recommendation results, it has been surprisingly overlooked in the literature compared to the accuracy of the recommenda…
Top-Personalized-K Recommendation
Wonbin Kweon, SeongKu Kang, Sanghwan Jang +1
The conventional top-K recommendation, which presents the top-K items with the highest ranking scores, is a common practice for generating personalized ranking lists. However, is t…
Distillation from Heterogeneous Models for Top-K Recommendation
SeongKu Kang, Wonbin Kweon, Dongha Lee +3
Recent recommender systems have shown remarkable performance by using an ensemble of heterogeneous models. However, it is exceedingly costly because it requires resources and infer…