2 citations · 2 across the 3 of their papers we have counts for
3 papers
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.IR2024★ 2 cited
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…
cs.IR2023
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…