5 papers
SymCERE: Symmetric Contrastive Learning for Robust Review-Enhanced Recommendation
Toyotaro Suzumura, Hisashi Ikari, Hiroki Kanezashi +2
Modern recommendation systems fuse user behavior graphs and review texts but often encounter a "Fusion Gap" caused by False Negatives, Popularity Bias, and Signal Ambiguity. We pro…
GEFM: Graph-Enhanced EEG Foundation Model
Limin Wang, Toyotaro Suzumura, Hiroki Kanezashi
Electroencephalography (EEG) signals provide critical insights for applications in disease diagnosis and healthcare. However, the scarcity of labeled EEG data poses a significant c…
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning
Toyotaro Suzumura, Hiroki Kanezashi, Shotaro Akahori
In diagnosing neurological disorders from electroencephalography (EEG) data, foundation models such as Transformers have been employed to capture temporal dynamics. Additionally, G…
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
LLM-jp, :, Akiko Aizawa +80
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and stron…
Multimodal Point-of-Interest Recommendation
Yuta Kanzawa, Toyotaro Suzumura, Hiroki Kanezashi +2
Large Language Models are applied to recommendation tasks such as items to buy and news articles to read. Point of Interest is quite a new area to sequential recommendation based o…