3 papers
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.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.IR2024
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…