Graph-Theoretic One-Class Collaborative Filtering using Signed Random Walk with Restart. - Yeon-Chang Lee; Daeyoung Park; Jiwon Son; Taeho Kim 003; Sang-Wook Kim (BigComp, 2020)

M-BPR: A novel approach to improving BPR for recommendation with multi-type pair-wise preferences. - Yeon-Chang Lee; Taeho Kim 003; Jaeho Choi; Xiangnan He 001; Sang-Wook Kim (Inf. Sci., 2021)

Exploiting uninteresting items for effective graph-based one-class collaborative filtering. - Yeon-Chang Lee; Jiwon Son; Taeho Kim 003; Daeyoung Park; Sang-Wook Kim (J. Supercomput., 2021)

Is It Enough Just Looking at the Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation. - Taeho Kim 003; Yungi Kim; Yeon-Chang Lee; Won-Yong Shin; Sang-Wook Kim (CIKM, 2022)

AiRS: A Large-Scale Recommender System at NAVER News. - Hongjun Lim; Yeon-Chang Lee; Jin-Seo Lee; Sanggyu Han; Seunghyeon Kim; Yeon Jeong Jeong; Changbong Kim; Jaehun Kim; Sunghoon Han; Solbi Choi; Hanjong Ko; Dokyeong Lee; et al. (ICDE, 2022)

LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation. - Taeho Kim 003; Juwon Yu; Won-Yong Shin; Hyunyoung Lee; Ji-Hui Im; Sang-Wook Kim (KDD, 2023)

ESPRESSO: An Effective Approach to Passage Retrieval for High-Quality Conversational Recommender Systems. - Taeho Kim 003; Hyeongjun Jang; Juwon Yu; Taeuk Kim; Hyunyoung Lee; Ji-Hui Im; Sang-Wook Kim (AAAI, 2025)