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Mingming Yang

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2 papers

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EAAI Journal 2026 Journal Article

Multi-view knowledge graph recommendation with dynamic transformer modeling

Mengting Zhang
Man Li
Wenming Cao
Mingming Yang
Bing Li
Guangzhen Zhu
Lu Cheng

Recommender systems are essential for alleviating information overload but continue to face challenges such as data sparsity, cold-start issues, and noisy knowledge graph structures. To address these problems, we propose a novel recommendation framework that integrates a Dynamic Tanh Transformer (DyT-Transformer), a multi-view knowledge graph propagation strategy, and an inter-layer attention mechanism. The DyT-Transformer enhances user-item representations by dynamically weighting key features without normalization layers, improving stability and efficiency. Multi-view propagation is employed to generate diverse graph views through randomized perturbations, which mitigates noise and improves robustness. Inter-layer attention adaptively aggregates embedding across propagation depths to capture hierarchical semantics. Extensive experiments on five real-world datasets, including Book-Crossing, MovieLens-1M, Amazon-Book, Yelp2018, and Alibaba, demonstrate that the proposed model consistently outperforms state-of-the-art baselines in both click-through rate prediction and Top-K recommendation tasks. These results confirm the effectiveness and scalability of combining DyT-Transformer with multi-view knowledge graph learning for robust recommendation. We have made the source code of our proposed model available at https: //github. com/tttterter/MVLDT.

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NeurIPS Conference 2024 Conference Paper

Benchmarking LLMs via Uncertainty Quantification

Fanghua Ye
Mingming Yang
Jianhui Pang
Longyue Wang
Derek F. Wong
Emine Yilmaz
Shuming Shi
Zhaopeng Tu

The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods. However, current evaluation platforms, such as the widely recognized HuggingFace open LLM leaderboard, neglect a crucial aspect -- uncertainty, which is vital for thoroughly assessing LLMs. To bridge this gap, we introduce a new benchmarking approach for LLMs that integrates uncertainty quantification. Our examination involves nine LLMs (LLM series) spanning five representative natural language processing tasks. Our findings reveal that: I) LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III) Instruction-finetuning tends to increase the uncertainty of LLMs. These results underscore the significance of incorporating uncertainty in the evaluation of LLMs. Our implementation is available at https: //github. com/smartyfh/LLM-Uncertainty-Bench.

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