Hengshu Zhu
9 أوراق في مجموعة PaperMetrix
أوراق هذا المؤلف
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Interactive Attention Transfer Network for Cross-Domain Sentiment Classification
2019 · Proceedings of the AAAI Conference on Artificial Intelligence
Cross-domain sentiment classification refers to utilizing useful knowledge in the source domain to help sentiment classification in the target domain which has few or no labeled data. Most existing methods mainly concentrate on extracting common …
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A Survey on Knowledge Graph-Based Recommender Systems
2020 · arXiv (Cornell University)
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems …
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Towards Robust Knowledge Graph Embedding via Multi-Task Reinforcement Learning
2021 · IEEE Transactions on Knowledge and Data Engineering
Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in AI-related applications. Despite the large sizes, existing KGs are far from complete and comprehensive. In order to continuously enrich KGs, automatic knowledge construction and …
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Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph
2023
Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in …
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A Survey on Knowledge Graph-Based Recommender Systems : Extended Abstract
2023
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems …
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Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution
2023 · arXiv (Cornell University)
Self-interpreting neural networks have garnered significant interest in research. Existing works in this domain often (1) lack a solid theoretical foundation ensuring genuine interpretability or (2) compromise model expressiveness. In response, we formulate a generic …
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Collaboration-Aware Hybrid Learning for Knowledge Development Prediction
2024
In recent years, the rise of online Knowledge Management Systems (KMSs) has significantly improved work efficiency in enterprises. Knowledge development prediction, as a critical application within these online platforms, enables organizations to proactively address knowledge …
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A Comprehensive Survey on Self-Interpretable Neural Networks
2025 · arXiv (Cornell University)
Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making scenarios. Post-hoc interpretability, which provides explanations for pre-trained models, is often at risk …
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Person-Job Fit
2018 · ACM Transactions on Management Information Systems
Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it …