Yuma Dose

According to our database, Yuma Dose authored at least 9 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Graph Contrastive Learning with Popularity and Neighborhood Awareness for Long-Tail Item Recommendation.

Yuma Dose, Takahiro Hara

Proceedings of the MultiMedia Modeling, 2026

2025

HGCL: A Hypergraph Contrastive Learning Framework with Graph Structure Learning for Recommendation.

Yuma Dose, Shuichiro Haruta, Yihong Zhang, Takahiro Hara

J. Inf. Process., 2025

A News Recommendation Framework Utilizing ChatGPT: Estimating Target Audience and News Categories.

Yoshiyuki Maekawa, Shuichiro Haruta, Yuma Dose, Takahiro Hara

Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, 2025

Dynamic Peak-Aware Loss for Zero-Shot Occupancy Estimation.

Jo Masuda, Takahiro Hara, Takuya Maekawa, Yuma Dose, Kazuki Shimazaki, Teppei Seguchi, Takayuki Kikuchi, Kenshiro Kato

Proceedings of the 37th IEEE International Conference on Tools with Artificial Intelligence, 2025

Robust Spatio-Temporal Graph Convolutional Networks for Headcount Prediction.

Ryusei Iwasa, Yuma Dose, Takahiro Hara, Takuya Maekawa, Kazuki Shimazaki, Teppei Seguchi, Takayuki Kikuchi, Kenshiro Kato

Proceedings of the 37th IEEE International Conference on Tools with Artificial Intelligence, 2025

MMGSL: Multi-Modal Graph Structure Learning for Recommendation.

Yoshiyuki Sone, Yuma Dose, Takahiro Hara, Takuya Maekawa, Kazuki Shimazaki, Teppei Seguchi, Takayuki Kikuchi, Kenshiro Kato

Proceedings of the IEEE International Conference on Big Data, 2025

2024

INCL: A Graph-based Recommendation Model Using Contrastive Learning for Inductive Scenario.

Yuma Dose, Shuichiro Haruta, Takahiro Hara

J. Inf. Process., 2024

Hypergraph Contrastive Learning with Graph Structure Learning for Recommendation.

Yuma Dose, Shuichiro Haruta, Yihong Zhang, Takahiro Hara

Proceedings of the International Conference on Machine Learning and Applications, 2024

2023

A Graph-Based Recommendation Model Using Contrastive Learning for Inductive Scenario.

Yuma Dose, Shuichiro Haruta, Takahiro Hara

Proceedings of the International Conference on Machine Learning and Applications, 2023