A Survey of Graph Neural Networks for Social Recommender Systems
- URL: http://arxiv.org/abs/2212.04481v3
- Date: Wed, 1 May 2024 15:24:47 GMT
- Title: A Survey of Graph Neural Networks for Social Recommender Systems
- Authors: Kartik Sharma, Yeon-Chang Lee, Sivagami Nambi, Aditya Salian, Shlok Shah, Sang-Wook Kim, Srijan Kumar,
- Abstract summary: Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions and the user-to-user social relations.
With the advance of graph neural networks (GNN), many GNN-based SocialRS methods have been developed recently.
We conduct a comprehensive and systematic review of the literature on GNN-based SocialRS methods.
- Score: 38.79810157156386
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of graph neural networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 84 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (preferred reporting items for systematic reviews and meta-analyses). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder notations, 2 groups of decoder notations, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions. GitHub repository with the curated list of papers are available at https://github.com/claws-lab/awesome-GNN-social-recsys.
Related papers
- Dual Policy Learning for Aggregation Optimization in Graph Neural
Network-based Recommender Systems [4.026354668375411]
We propose a novel reinforcement learning-based message passing framework for recommender systems.
This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning.
Our results show that our proposed framework significantly enhances the recent base model, improving nDCG and Recall by up to 63.7% and 42.9%, respectively.
arXiv Detail & Related papers (2023-02-21T09:47:27Z) - Evidential Temporal-aware Graph-based Social Event Detection via
Dempster-Shafer Theory [76.4580340399321]
We propose ETGNN, a novel Evidential Temporal-aware Graph Neural Network.
We construct view-specific graphs whose nodes are the texts and edges are determined by several types of shared elements respectively.
Considering the view-specific uncertainty, the representations of all views are converted into mass functions through evidential deep learning (EDL) neural networks.
arXiv Detail & Related papers (2022-05-24T16:22:40Z) - Federated Social Recommendation with Graph Neural Network [69.36135187771929]
We propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem.
We devise a novel framework textbfFedrated textbfSocial recommendation with textbfGraph neural network (FeSoG)
arXiv Detail & Related papers (2021-11-21T09:41:39Z) - Harnessing the Power of Ego Network Layers for Link Prediction in Online
Social Networks [0.734084539365505]
Predictions are typically based on unsupervised or supervised learning.
We argue that richer information about personal social structure of individuals might lead to better predictions.
We show that social-awareness generally provides significant improvements in the prediction performance.
arXiv Detail & Related papers (2021-09-19T18:49:10Z) - Recurrent Graph Neural Networks for Rumor Detection in Online Forums [14.868643774881624]
This work presents techniques for classifying linked content spread on forum websites using user interaction signals alone.
Online forums such as Reddit do not have a user-generated social graph, which is assumed in social network behavioral-based classification settings.
We show how to obtain a derived social graph, and use this graph, Reddit post sequences, and comment trees as inputs to a Recurrent Graph Neural Network (R-GNN) encoder.
arXiv Detail & Related papers (2021-08-08T01:34:49Z) - Visualizing Graph Neural Networks with CorGIE: Corresponding a Graph to
Its Embedding [16.80197065484465]
We propose an approach to corresponding an input graph to its node embedding (aka latent space)
We develop an interactive multi-view interface called CorGIE to instantiate the abstraction.
We present how to use CorGIE in two usage scenarios, and conduct a case study with two GNN experts.
arXiv Detail & Related papers (2021-06-24T08:59:53Z) - Learning Intents behind Interactions with Knowledge Graph for
Recommendation [93.08709357435991]
Knowledge graph (KG) plays an increasingly important role in recommender systems.
Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents.
We propose a new model, Knowledge Graph-based Intent Network (KGIN)
arXiv Detail & Related papers (2021-02-14T03:21:36Z) - Knowledge-Preserving Incremental Social Event Detection via
Heterogeneous GNNs [72.09532817958932]
We propose a novel Knowledge-Preserving Incremental Heterogeneous Graph Neural Network (KPGNN) for incremental social event detection.
KPGNN models complex social messages into unified social graphs to facilitate data utilization and explores the expressive power of GNNs for knowledge extraction.
It also leverages the inductive learning ability of GNNs to efficiently detect events and extends its knowledge from previously unseen data.
arXiv Detail & Related papers (2021-01-21T17:56:57Z) - Recursive Social Behavior Graph for Trajectory Prediction [49.005219590582676]
We formulate social representations supervised by group-based annotations into a social behavior graph, called Recursive Social Behavior Graph.
With the guidance of Recursive Social Behavior Graph, we surpass state-of-the-art method on ETH and UCY dataset for 11.1% in ADE and 10.8% in FDE.
arXiv Detail & Related papers (2020-04-22T06:01:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.