2 citations · 2 across the 10 of their papers we have counts for
10 papers
Text-attributed Graph Condensation via Text Selection and Attribute Matching
Haowei Han, Yuxiang Wang, Guojia Wan +5
Text-Attributed Graph (TAG) is an important type of graph structured data, where each node has a text description. TAG models usually train a Graph Neural Network (GNN) and languag…
MiCU: End-to-End Smart Home Command Understanding with Large Language Model
Haowei Han, Kexin Hu, Weiwei Cai +6
Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perform well on precise utterances…
End-to-End Semantic ID Generation for Generative Advertisement Recommendation
Jie Jiang, Xinxun Zhang, Enming Zhang +8
Generative Recommendation (GR) has excelled by framing recommendation as next-token prediction. This paradigm relies on Semantic IDs (SIDs) to tokenize large-scale items into discr…
DevPiolt: Operation Recommendation for IoT Devices at Xiaomi Home
Yuxiang Wang, Siwen Wang, Haowei Han +10
Operation recommendation for IoT devices refers to generating personalized device operations for users based on their context, such as historical operations, environment informatio…
How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG
Qiming Zeng, Hao Luo, Yuhao Lin +5
By retrieving contexts from knowledge graphs, graph-based retrieval-augmented generation (GraphRAG) enhances large language models (LLMs) to generate quality answers for user quest…
Guiding LLM-based Smart Contract Generation with Finite State Machine
Hao Luo, Yuhao Lin, Xiao Yan +5
Smart contract is a kind of self-executing code based on blockchain technology with a wide range of application scenarios, but the traditional generation method relies on manual co…
Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph
Yuxiang Wang, Xiao Yan, Shiyu Jin +6
Text-attributed graph (TAG) provides a text description for each graph node, and few- and zero-shot node classification on TAGs have many applications in fields such as academia an…
MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan
Yuxiang Wang, Chi Ma, Xiao Yan +15
Recommendation is crucial for both user experience and company revenue in Meituan as a leading lifestyle company, and generative recommendation models (GRMs) are shown to produce q…
Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph
Yuxiang Wang, Xiao Yan, Shiyu Jin +6
Text-attributed graph (TAG) is an important type of graph structured data with text descriptions for each node. Few- and zero-shot node classification on TAGs have many application…
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
Yuxiang Wang, Xiao Yan, Chuang Hu +5
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learn…