A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering

Guo, Ronghui; Li, Xunkai; Hu, Youpeng; Wu, Yixuan; Xiong, Xin; Qu, Mingli

doi:10.1109/access.2021.3083600

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Inspired by the latest progress in node/graph classification methods [11], this paper is based on the method of the spectral graph theory and uses the multivariate implicit information in the field of graphs to overcome the abovementioned shortcomings and challenges. Specifically, in order to overcome the difficulty of learning recommendation directly from the spectral domain, this paper proposes a new spectral convolution operation, which is approximated by the Chebyshev first-order truncation and dynamically amplifies or attenuates each frequency domain [12].…”

Section: Introductionmentioning

confidence: 99%

Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

Wang

Zeng

Song

et al. 2022

Wireless Communications and Mobile Computing

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To solve the abovementioned problem, we propose a collaborative filtering recommendation algorithm that incorporates singular value decomposition (SVD) and Chebyshev truncation in spectral domain convolution. Firstly, the SVD algorithm is used to optimize the adjacency matrix, mine the potential association information between users and items, and expand the user-item adjacency matrix. Finally, based on the MovieLens-1M public dataset, the proposed algorithm (CBSVD-SCF) is compared with other commonly used algorithms. The results show that the article optimizes the recommendation effect of the algorithm based on the traditional collaborative filtering algorithm by combining the temporal order and sequence of user interaction information, as well as the popularity of items and the activity of users; the experimental results on MovieLens show that the optimized collaborative filtering recommendation algorithm can effectively improve the recommendation effect.

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Section: Introductionmentioning

confidence: 99%

Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

Wang

Zeng

Song

et al. 2022

Wireless Communications and Mobile Computing

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“…(5) The LII-GCCF (linear transformation, initial residual, identity mapping, graph convolutional collaborative filtering) model [33] is an improved recommendation algorithm using GCN.…”

Section: Compare the Modelmentioning

confidence: 99%

Recommendation System Based on Perceptron and Graph Convolution Network

Lian,

Yin,

Wang

2024

CMC

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The relationship between users and items, which cannot be recovered by traditional techniques, can be extracted by the recommendation algorithm based on the graph convolution network. The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data. This paper presents a new approach to address such issues, utilizing the graph convolution network to extract association relations. The proposed approach mainly includes three modules: Embedding layer, forward propagation layer, and score prediction layer. The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer. The forward propagation layer designs two parallel graph convolution networks with self-connections, which extract higher-order association relevance from users and items separately by multi-layer graph convolution. Furthermore, the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion, capturing more comprehensive association relevance between users and items as input for the score prediction layer. The score prediction layer introduces MLP (multi-layer perceptron) to conduct nonlinear feature interaction between users and items, respectively. Finally, the prediction score of users to items is obtained. The recall rate and normalized discounted cumulative gain were used as evaluation indexes. The proposed approach effectively integrates higher-order information in user entries, and experimental analysis demonstrates its superiority over the existing algorithms.

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Residual Graph Convolution Collaborative Filtering with Asymmetric neighborhood aggregation

Wang,

Qin,

2024

Neural Comput & Applic

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A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering

Cited by 5 publications

References 26 publications

Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev

Recommendation System Based on Perceptron and Graph Convolution Network

Residual Graph Convolution Collaborative Filtering with Asymmetric neighborhood aggregation

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