Fuzheng Zhang And Boyfriend 2019 (2024)

1. ‪Fuzheng Zhang(张富峥)‬ - ‪Google Scholar‬

  • Fuzheng Zhang(张富峥) 「快意」大模型中心负责人,快手 Verified email at mail.ustc.edu.cn - Homepage

  • ‪「快意」大模型中心负责人,快手‬ - ‪‪Cited by 13,715‬‬ - ‪Large Language Model‬ - ‪Multimodal Large Language Model‬

‪Fuzheng Zhang(张富峥)‬ - ‪Google Scholar‬

2. Zhang Fu Zheng (张福正) - MyDramaList

Zhang Fu Zheng (张福正) - MyDramaList

3. ‪Fuzheng Zhang(张富峥)‬ - ‪Google Scholar‬

  • 「快意」大模型中心负责人,快手 - ‪‪引用: 13679 件‬‬ - ‪Large Language Model‬ - ‪Multimodal Large Language Model‬

  • ‪「快意」大模型中心负责人,快手‬ - ‪‪引用: 13,715 件‬‬ - ‪Large Language Model‬ - ‪Multimodal Large Language Model‬

‪Fuzheng Zhang(张富峥)‬ - ‪Google Scholar‬

4. Fuzheng Zhang - Semantic Scholar

Fuzheng Zhang - Semantic Scholar

5. Zhang Fuzheng - The Movie Database

  • Fuzheng Zhang is known as an Actor. Some of his work includes Green Snake, A Record of a Mortal's Journey to Immortality, Word of Honor, The Magical Chef of ...

  • Fuzheng Zhang is known as an Actor. Some of his work includes Green Snake, A Record of a Mortal's Journey to Immortality, Word of Honor, The Magical Chef of Ice and Fire, The Young Brewmaster's Adventure, How dare you!?, My Girlfriend is an Alien, and Legendary Twins.

Zhang Fuzheng - The Movie Database

6. Fuzheng Zhang - dblp

  • List of computer science publications by Fuzheng Zhang.

  • List of computer science publications by Fuzheng Zhang

7. [1905.04413] Knowledge-aware Graph Neural Networks with Label ...

  • 11 mei 2019 · Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.

  • Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could help improve recommender systems. However, existing approaches in this domain rely on manual feature engineering and do not allow for an end-to-end training. Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations. Conceptually, our approach computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user. This way we transform the knowledge graph into a user-specific weighted graph and then apply a graph neural network to compute personalized item embeddings. To provide better inductive bias, we rely on label smoothness assumption, which posits that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores. Label smoothness provides regularization over the edge weights and we prove that it is equivalent to a label propagation scheme on a graph. We also develop an efficient implementation that shows strong scalability with respect to the knowledge graph size. Experiments on four datasets show that our method outperforms state of the art baselines. KGNN-LS also achieves strong performance in cold-start scenarios where user-item interactions are sparse.

[1905.04413] Knowledge-aware Graph Neural Networks with Label ...

8. Fuzheng Zhang | Papers With Code

  • Papers by Fuzheng Zhang with links to code and results.

Fuzheng Zhang | Papers With Code
Fuzheng Zhang And Boyfriend 2019 (2024)
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