80 citations · 158 across the 31 of their papers we have counts for
6 papers · 2 filters
Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category Preferences
Gwangseok Han, Wonbin Kweon, Minsoo Kim +1
Diversity control is an important task to alleviate bias amplification and filter bubble problems. The desired degree of diversity may fluctuate based on users' daily moods or busi…
Continual Collaborative Distillation for Recommender System
Gyuseok Lee, SeongKu Kang, Wonbin Kweon +1
Knowledge distillation (KD) has emerged as a promising technique for addressing the computational challenges associated with deploying large-scale recommender systems. KD transfers…
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…