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Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lie...
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Multi-Component Graph Convolutional Collaborative Filtering
The interactions of users and items in recommender system could be natur...
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Multi-Graph Convolution Collaborative Filtering
Personalized recommendation is ubiquitous, playing an important role in ...
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RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding
Graph Convolution Network (GCN) has attracted significant attention and ...
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Disentangled Graph Collaborative Filtering
Learning informative representations of users and items from the interac...
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Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopte...
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Deoscillated Graph Collaborative Filtering
Collaborative Filtering (CF) signals are crucial for a Recommender Syste...
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Heterogeneous Graph Collaborative Filtering
Graph-based collaborative filtering (CF) algorithms have gained increasing attention. Existing work in this literature usually models the user-item interactions as a bipartite graph, where users and items are two isolated node sets and edges between them indicate their interactions. Then, the unobserved preference of users can be exploited by modeling high-order connectivity on the bipartite graph. In this work, we propose to model user-item interactions as a heterogeneous graph which consists of not only user-item edges indicating their interaction but also user-user edges indicating their similarity. We develop heterogeneous graph collaborative filtering (HGCF), a GCN-based framework which can explicitly capture both the interaction signal and similarity signal through embedding propagation on the heterogeneous graph. Since the heterogeneous graph is more connected than the bipartite graph, the sparsity issue can be alleviated and the demand for expensive high-order connectivity modeling can be lowered. Extensive experiments conducted on three public benchmarks demonstrate its superiority over the state-of-the-arts. Further analysis verifies the importance of user-user edges in the graph, justifying the rationality and effectiveness of HGCF.
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