Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Mine, Tsunenori"'
Autor:
Fateen, Menna, Mine, Tsunenori
Recent advances in large language models (LLMs) have shown promise for scalable educational applications, but their use in dialog-based tutoring systems remains challenging due to the need for effective pedagogical strategies and the high costs assoc
Externí odkaz:
http://arxiv.org/abs/2410.19231
Automatic short answer scoring (ASAS) helps reduce the grading burden on educators but often lacks detailed, explainable feedback. Existing methods in ASAS with feedback (ASAS-F) rely on fine-tuning language models with limited datasets, which is res
Externí odkaz:
http://arxiv.org/abs/2409.20042
Autor:
Wang, Bo, Mine, Tsunenori
This paper presents a novel and comprehensive solution to enhance both the robustness and efficiency of question answering (QA) systems through supervised contrastive learning (SCL). Training a high-performance QA system has become straightforward wi
Externí odkaz:
http://arxiv.org/abs/2407.09011
Modern recommender systems heavily rely on high-quality representations learned from high-dimensional sparse data. While significant efforts have been invested in designing powerful algorithms for extracting user preferences, the factors contributing
Externí odkaz:
http://arxiv.org/abs/2406.12032
It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a graph filter or followed by a low-rank mapping optimized based on supe
Externí odkaz:
http://arxiv.org/abs/2406.08827
Autor:
Wang, Wei, Lin, Yujie, Ren, Pengjie, Chen, Zhumin, Mine, Tsunenori, Zhao, Jianli, Zhao, Qiang, Zhang, Moyan, Ben, Xianye, Li, Yujun
Sequential recommendation has attracted a lot of attention from both academia and industry, however the privacy risks associated to gathering and transferring users' personal interaction data are often underestimated or ignored. Existing privacy-pres
Externí odkaz:
http://arxiv.org/abs/2401.04423
With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-based methods rigorously stick to a common GCN learning paradigm and suff
Externí odkaz:
http://arxiv.org/abs/2208.12689
As much as Graph Convolutional Networks (GCNs) have shown tremendous success in recommender systems and collaborative filtering (CF), the mechanism of how they, especially the core components (\textit{i.e.,} neighborhood aggregation) contribute to re
Externí odkaz:
http://arxiv.org/abs/2204.11346
Autor:
Peng, Shaowen, Mine, Tsunenori
Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems. However, GCN still suffers from many issues such as training difficulties, over-smoothing, vulnerable to adversarial at
Externí odkaz:
http://arxiv.org/abs/2004.14734