Showing cs.IRShow all
3 papers · 1 filter
cs.IR2025
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…
cs.IR2025
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…
cs.IR2025
Similarity-Based Supervised User Session Segmentation Method for Behavior Logs
Yongzhi Jin, Kazushi Okamoto, Kei Harada +2
In information recommendation, a session refers to a sequence of user actions within a specific time frame. Session-based recommender systems aim to capture short-term preferences…