Collective Matrix Completion via Graph Extraction

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-w...

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Bibliographic Details
Published in:IEEE signal processing letters 2024, Vol.31, p.2620-2624
Main Authors: Zhan, Tong, Mao, Xiaojun, Wang, Jian, Wang, Zhonglei
Format: Article
Language:English
Subjects:
Artificial neural networks
Collective matrix completion
Data mining
Feature extraction
graph extraction
graph neural network
recommendation system
Recommender systems
Signal processing algorithms
Training
Transforms
Vectors
Citations: Items that this one cites
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Description
Summary: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.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2024.3460483