8 citations · 10 across the 7 of their papers we have counts for
9 papers
Trigger Value Selection in Clean-Label Malware Backdoors: An Effectiveness-Detectability Analysis
Tri Budi Sabdo Utomo, Kazushi Okamoto, Koki Karube +2
Clean-label backdoor poisoning is a threat to machine learning-based malware classifiers because poisoned samples can be inserted into training data without changing their labels.…
LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework
Ferry Afit Kurniawan, Kazushi Okamoto, Koki Karube +2
Cyber threat intelligence (CTI) relies on the timely identification of emerging threats, enabling organizations and security analysts to support situational awareness and decision-…
Estimation of Fireproof Structure Class and Construction Year for Disaster Risk Assessment
Hibiki Ayabe, Kazushi Okamoto, Koki Karube +2
Structural fireproof classification is vital for disaster risk assessment and insurance pricing in Japan. However, key building metadata such as construction year and structure typ…
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…