Collaborative Filtering Model of Graph Neural Network Based on Random Walk

Collaborative Filtering Model of Graph Neural Network Based on Random Walk

This paper proposes a novel graph neural network recommendation method to alleviate the user cold-start problem caused by too few relevant items in personalized recommendation collaborative filtering. A deep feedforward neural network is constructed to transform the bipartite graph of user–item inte...

Full description

Bibliographic Details
Main Authors:	Jiahao Wang, Hongyan Mei, Kai Li, Xing Zhang, Xin Chen
Format:	Article
Language:	English
Published:	MDPI AG 2023-01-01
Series:	Applied Sciences
Subjects:	recommender systems graph neural networks spectral domain graph convolution collaborative filtering
Online Access:	https://www.mdpi.com/2076-3417/13/3/1786

Similar Items

Region-aware neural graph collaborative filtering for personalized recommendation
by: Shengwen Li, et al.
Published: (2022-12-01)

A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering
by: Ronghui Guo, et al.
Published: (2021-01-01)

Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph
by: Dehai Zhang, et al.
Published: (2020-05-01)

Efficient Graph Collaborative Filtering via Contrastive Learning
by: Zhiqiang Pan, et al.
Published: (2021-07-01)

Combining Social Relations and Interaction Data in Recommender System With Graph Convolution Collaborative Filtering
by: Tin T. Tran, et al.
Published: (2023-01-01)

SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering
by: Heng Lu, et al.
Published: (2024-12-01)

A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method
by: Min Ma, et al.
Published: (2022-11-01)

AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation
by: Jie Li, et al.
Published: (2025-03-01)

Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding
by: Zeinab Shokrzadeh, et al.
Published: (2024-01-01)

Unveiling the silent majority: stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networks
by: Zhiwei Zhou, et al.
Published: (2024-04-01)

A Multimedia Graph Collaborative Filter
by: Jingzhou Sun, et al.
Published: (2022-01-01)

DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
by: Yi Zuo, et al.
Published: (2023-02-01)

Knowledge Graph Embedding Based Collaborative Filtering
by: Yuhang Zhang, et al.
Published: (2020-01-01)

Research on Neural Graph Collaborative Filtering Recommendation Model Fused With Item Temporal Sequence Relationships
by: Dewen Seng, et al.
Published: (2022-01-01)

Cross-Grained Neural Collaborative Filtering for Recommendation
by: Chuntai Li, et al.
Published: (2024-01-01)

Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation
by: Zhi-Yuan Li, et al.
Published: (2023-06-01)

Prediction of Students’ Grade by Combining Educational Knowledge Graph and Collaborative Filtering
by: Yiwen Zhang, et al.
Published: (2024-01-01)

Neural Collaborative Filtering for Social Recommendation Algorithm Based on Graph Attention
by: ZHANG Qi, YU Shuangyuan, YIN Hongfeng, XU Baomin
Published: (2023-02-01)

A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering
by: Bo Jiang, et al.
Published: (2021-01-01)

Collaborative Filtering Recommendation-Based Random Negative Sampling and Graph Attention
by: Weiqiang Li, et al.
Published: (2025-01-01)

An adaptive preference retention collaborative filtering algorithm based on graph convolutional method
by: Bingjie Zhang, et al.
Published: (2023-01-01)

A deep learning knowledge graph neural network for recommender systems
by: Gurinder Kaur, et al.
Published: (2023-12-01)

CrossDomain Recommendation Based on MetaData Using Graph Convolution Networks
by: Rabia Khan, et al.
Published: (2023-01-01)

Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive Learning
by: Jifeng Dong, et al.
Published: (2024-06-01)

MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering
by: Ahlem Drif, et al.
Published: (2022-08-01)

Survey of Recommender Systems Based on Graph Learning
by: CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan
Published: (2022-09-01)

Adaptive Learning User Implicit Trust Behavior Based on Graph Convolution Network
by: Dewen Seng, et al.
Published: (2021-01-01)

Deep Disentangled Collaborative Filtering with Graph Global Information
by: HAO Jingyu, WEN Jingxuan, LIU Huafeng, JING Liping, YU Jian
Published: (2023-01-01)

A multi-intent-aware recommendation algorithm based on interactive graph convolutional networks
by: Junsan Zhang, et al.
Published: (2024-03-01)

Fast Library Recommendation in Software Dependency Graphs with Symmetric Partially Absorbing Random Walks
by: Emmanouil Krasanakis, et al.
Published: (2022-04-01)

Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association
by: Jihwan Ha
Published: (2025-01-01)

Graph Transformer Collaborative Filtering Method for Multi-Behavior Recommendations
by: Wenhao Zhu, et al.
Published: (2022-08-01)

Light Graph Convolutional Collaborative Filtering With Multi-Aspect Information
by: Denghua Mei, et al.
Published: (2021-01-01)

IcaGCN: Model Intents via Coactivated Graph Convolution Network for Recommendation
by: Jingxue Zhang, et al.
Published: (2023-01-01)

Using an Exponential Random Graph Model to Recommend Academic Collaborators
by: Hailah Al-Ballaa, et al.
Published: (2019-06-01)

Graph neural network recommendation algorithm based on improved dual tower model
by: Qiang He, et al.
Published: (2024-02-01)

Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs
by: Chensheng Li, et al.
Published: (2020-01-01)

SimDCL: dropout-based simple graph contrastive learning for recommendation
by: YuHao Xu, et al.
Published: (2023-02-01)

Graph contrast learning for recommendation based on relational graph convolutional neural network
by: Xiaoyang Liu, et al.
Published: (2024-10-01)

CRecSys: A Context-Based Recommender System Using Collaborative Filtering and LOD
by: Vineet K. Sejwal, et al.
Published: (2020-01-01)