GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation

@article{Jiajia2022GDSRecGD,
  title={GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation},
  author={Chen Jiajia and Xin Xin and Xian-Feng Liang and Xiangnan He and Jun Liu},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.09948}
}
—Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented as graph-structured data and thus utilizing graph neural networks (GNNs) for social recommendation has become a promising research direction. However, existing graph-based methods fails to consider the bias offsets of users (items). For example, a low rating from a fastidious user may not imply a negative attitude… 

References

SHOWING 1-10 OF 56 REFERENCES
Graph Neural Networks for Social Recommendation
TLDR
This paper provides a principled approach to jointly capture interactions and opinions in the user-item graph and proposes the framework GraphRec, which coherently models two graphs and heterogeneous strengths for social recommendations.
Social Boosted Recommendation With Folded Bipartite Network Embedding
TLDR
A novel embedding method for general bipartite graphs is proposed, which defines inter-class message passing between explicit relations and intra-class messages passing between implicit higher-order relations via a novel sequential modelling paradigm, and suggests that higher- order implicit relationship among users is beneficial to improving social recommendation.
A Neural Influence Diffusion Model for Social Recommendation
  • Le Wu
  • Computer Science
  • 2019
TLDR
A deep influence propagation model is proposed to stimulate how users are influenced by the recursive social diffusion process for social recommendation, and design a layer-wise influence propagation structure to model how users’ latent embeddings evolve as thesocial diffusion process continues.
A Neural Influence Diffusion Model for Social Recommendation
TLDR
A deep influence propagation model is proposed to stimulate how users are influenced by the recursive social diffusion process for social recommendation, with more than 13% performance improvements over the best baselines for top-10 recommendation on the two datasets.
Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs
TLDR
A unified model for collaborative filtering based on graph regularized weighted nonnegative matrix factorization, which has the ability to make use of content information and any additional information regarding user-user such as social trust network is proposed.
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
TLDR
DiffNet++ is proposed, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework that advances DiffNet by injecting these two network information for user embedding learning at the same time.
Modelling High-Order Social Relations for Item Recommendation
TLDR
This work proposes to directly factor social relations in the predictive model, aiming at learning better user embeddings to improve recommendation, and shows that the HOSR significantly outperforms recent graph regularization-based recommenders NSCR and IF-BPR.
A matrix factorization technique with trust propagation for recommendation in social networks
TLDR
A model-based approach for recommendation in social networks, employing matrix factorization techniques and incorporating the mechanism of trust propagation into the model demonstrates that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.
SoRec: social recommendation using probabilistic matrix factorization
TLDR
A factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records is proposed.
Enhance Social Recommendation with Adversarial Graph Convolutional Networks
TLDR
A deep adversarial framework based on graph convolutional networks (GCN) is proposed to address the problems of social recommender systems and adopts adversarial training to unify all the components by playing a Minimax game and ensure a coordinated effort to enhance recommendation performance.
...
...