• Corpus ID: 220250619

Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

@inproceedings{Yu2020GraphCN,
  title={Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters},
  author={Wenhui Yu and Zheng Qin},
  booktitle={ICML},
  year={2020}
}
\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage the \textit{original graph convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative \textbf{F}ilter (\textbf{LCF}) to… 
[PDF] Semantic Reader
34 Citations
Highly Influential Citations
2
Background Citations
11
Methods Citations
13
Results Citations
1

Figures and Tables from this paper

34 Citations

SimpleX: A Simple and Strong Baseline for Collaborative Filtering
TLDR
It is shown that the choice of loss function as well as negative sampling ratio is equivalently important, and a simple unified CF model, dubbed SimpleX is proposed, which can surpass most sophisticated state-of-the-art models by a large margin.
Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs
TLDR
A novel extension of the framework, MPNN-R (MPNN-Recursive) to handle recursively-structured data is proposed, motivated by real-world datasets.
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
—Spatiotemporal activity prediction, aiming to predict user activities at a specific location and time, is crucial for applications like urban planning and mobile advertising. Existing solutions based…
Automatically Discovering User Consumption Intents in Meituan
TLDR
This work designs the AutoIntent system, consisting of the disentangled intent encoder and intent discovery decoder, and proposes to build intent-pair pseudo labels based on the denoised feature similarities to transfer knowledge from known intents to new ones.
Low-Pass Graph Convolutional Network for Recommendation
TLDR
The bipartite graph is utilized to define the graph space directly and explore the best network structure based on experiments, which demonstrates the effectiveness of the proposed Low-pass Collaborative Filter Network.
Scalable and Effective Bipartite Network Embedding
TLDR
This paper proposes GEBE, a generic BNE framework achieving state-of-the-art performance on massive bipartite graphs, via four main algorithmic designs, and presents two generic measures to capture the multi-hop similarity/proximity between homogeneous/heterogeneous nodes respectively.
Less is More: Reweighting Important Spectral Graph Features for Recommendation
TLDR
This work proposes a new GCN learning scheme for recommendation by replacing neihgborhood aggregation with a simple yet effective Graph Denoising Encoder (GDE), which acts as a band pass filter to capture important graph features.
Handling information loss of graph convolutional networks in collaborative filtering
Knowledge Graph Random Neural Networks for Recommender Systems
A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources
...
...

References

SHOWING 1-10 OF 41 REFERENCES
Graph Neural Networks: A Review of Methods and Applications
Spectral collaborative filtering
TLDR
Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF.
Graph Convolutional Matrix Completion
TLDR
A graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph that shows competitive performance on standard collaborative filtering benchmarks and outperforms recent state-of-the-art methods.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
TLDR
This work presents a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
TLDR
This tutorial overview outlines the main challenges of the emerging field of signal processing on graphs, discusses different ways to define graph spectral domains, which are the analogs to the classical frequency domain, and highlights the importance of incorporating the irregular structures of graph data domains when processing signals on graphs.
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt.
Spectrum-enhanced Pairwise Learning to Rank
TLDR
Spectral features extracted from two hypergraph structures of the purchase records are introduced and used to model the users' preference and items' properties by incorporating them into a Matrix Factorization (MF) model, which outperform several state-of-the-art models significantly.
Neural Graph Collaborative Filtering
TLDR
This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
TLDR
A novel method based on highly efficient random walks to structure the convolutions and a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model are developed.
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
TLDR
This paper proposes a novel approach to overcome limitations of matrix completion techniques by using geometric deep learning on graphs, and applies this method on both synthetic and real datasets, showing that it outperforms state-of-the-art techniques.
...
...