20 citations · 70 across the 13 of their papers we have counts for
5 papers · 1 filter
Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?
Sein Kim, Hongseok Kang, Kibum Kim +6
Large Language Models (LLMs) have recently emerged as promising tools for recommendation thanks to their advanced textual understanding ability and context-awareness. Despite the c…
Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System
Sein Kim, Hongseok Kang, Seungyoon Choi +3
Collaborative filtering recommender systems (CF-RecSys) have shown successive results in enhancing the user experience on social media and e-commerce platforms. However, as CF-RecS…
Task Relation-aware Continual User Representation Learning
Sein Kim, Namkyeong Lee, Donghyun Kim +2
User modeling, which learns to represent users into a low-dimensional representation space based on their past behaviors, got a surge of interest from the industry for providing pe…
Collaborative Translational Metric Learning
Chanyoung Park, Donghyun Kim, Xing Xie +1
Recently, matrix factorization-based recommendation methods have been criticized for the problem raised by the triangle inequality violation. Although several metric learning-based…
Click-aware purchase prediction with push at the top
Chanyoung Park, Donghyun Kim, Min-Chul Yang +2
Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all no…