Graph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods.
Code (17)
Tasks
Collaborative FilteringLink PredictionMatrix CompletionRecommendation SystemsSimilar Papers 제목 키워드 기반
Convolutional Geometric Matrix Completion
Geometric matrix completion (GMC) has been proposed for recommendation by integrating the relationship (link) graphs among users/items into matrix completion (MC). Traditional GMC methods typically adopt graph regulariza…
Matrix CompletionMatrix Data Deep Decoder - Geometric Learning for Structured Data Completion
In this work, we present a fully convolutional end to end method to reconstruct corrupted sparse matrices of Non-Euclidean data. The classic example for such matrices is recommender systems matrices where the rows/column…
DecoderGraph Neural NetworkMatrix CompletionRecommendation SystemsConvolutional Graph-Tensor Net for Graph Data Completion
Graph data completion is a fundamentally important issue as data generally has a graph structure, e.g., social networks, recommendation systems, and the Internet of Things. We consider a graph where each node has a data …
Recommendation SystemsGeometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items …
Collaborative FilteringMatrix CompletionRecommendation SystemsGraph-Based Matrix Completion Applied to Weather Data
Low-rank matrix completion is the task of recovering unknown entries of a matrix by assuming that the true matrix admits a good low-rank approximation. Sometimes additional information about the variables is known, and i…
Low-Rank Matrix CompletionMatrix Completion