8 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.IR2025
Knowledge-Augmented Relation Learning for Complementary Recommendation with Large Language Models
Chihiro Yamasaki, Kai Sugahara, Kazushi Okamoto
Complementary recommendations play a crucial role in e-commerce by enhancing user experience through suggestions of compatible items. Accurate classification of complementary item…
cs.IR2025
Function-based Labels for Complementary Recommendation: Definition, Annotation, and LLM-as-a-Judge
Chihiro Yamasaki, Kai Sugahara, Yuma Nagi +1
Complementary recommendations enhance the user experience by suggesting items that are frequently purchased together while serving different functions from the query item. Inferrin…
cs.IR2023★ 8 cited
Hierarchical Matrix Factorization for Interpretable Collaborative Filtering
Kai Sugahara, Kazushi Okamoto
Matrix factorization (MF) is a simple collaborative filtering technique that achieves superior recommendation accuracy by decomposing the user-item interaction matrix into user and…