4 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…
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…
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…
A Completely Locale-independent Session-based Recommender System by Leveraging Trained Model
Yu Tokutake, Chihiro Yamasaki, Yongzhi Jin +2
In this paper, we propose a solution that won the 10th prize in the KDD Cup 2023 Challenge Task 2 (Next Product Recommendation for Underrepresented Languages/Locales). Our approach…