11 citations · 29 across the 10 of their papers we have counts for
10 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…
Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
Hyunjun Ju, SeongKu Kang, Dongha Lee +3
Recently, web platforms have been operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Doma…
Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy
SeongKu Kang, Shivam Agarwal, Bowen Jin +3
Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific ap…
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…
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…