| Autor: |
Liu, Fan, Zhao, Shuai, Cheng, Zhiyong, Nie, Liqiang, Kankanhalli, Mohan |
| Zdroj: |
ACM Transactions on Information Systems; Nov2024, Vol. 42 Issue 6, p1-24, 24p |
| Abstrakt: |
The article focuses on the Cluster-based Graph Collaborative Filtering (ClusterGCF), a novel recommendation model designed to enhance representation learning by addressing the challenges of high-order neighboring nodes and user interests. Topics include the introduction of a soft node clustering method that groups users and items, the construction of cluster-specific graphs to filter out noise and capture information and ClusterGCF's state-of-the-art performance across multiple datasets. |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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