8 citations · 10 across the 7 of their papers we have counts for
6 papers · 1 filter
Generation and annotation of item usage scenarios in e-commerce using large language models
Madoka Hagiri, Kazushi Okamoto, Koki Karube +2
Complementary recommendations suggest combinations of useful items that play important roles in e-commerce. However, complementary relationships are often subjective and vary among…
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…
A Universal Framework for Offline Serendipity Evaluation in Recommender Systems via Large Language Models
Yu Tokutake, Kazushi Okamoto, Kei Harada +2
Serendipity in recommender systems (RSs) has attracted increasing attention as a concept that enhances user satisfaction by presenting unexpected and useful items. However, evaluat…
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…
Can Large Language Models Assess Serendipity in Recommender Systems?
Yu Tokutake, Kazushi Okamoto
Serendipity-oriented recommender systems aim to counteract over-specialization in user preferences. However, evaluating a user's serendipitous response towards a recommended item c…
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…