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Collective Matrix Completion via Graph Extraction
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AI
DOI：10.1109/LSP.2024.3460483.png)
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摘要
En 中文
Collective matrix completion (CMC) offers a straightforward approach to dealing with data with entries from various sources. Benefiting from the joint structure in the collective matrix, CMC often achieves fast convergence. However, since CMC conducts matrix-level operations, it neglects the entry-wise information that can potentially be very useful for matrix completion. In this paper, to capture the entry-wise information, we propose a method called graph collective matrix completion (GCoMC). Specifically, our method integrates a graph pattern extraction module into CMC via a relational graph convolutional network. Experiments on simulated and real-world datasets show that our method significantly outperforms some existing counterparts.
Keyword:
Feature extraction
Data mining
Vectors
Transforms
Recommender systems
Training
Signal processing algorithms
Collective matrix completion
graph extraction
graph neural network
recommendation system
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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