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Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks [article]

Huance Xu, Chao Huang, Yong Xu, Lianghao Xia, Hao Xing, Dawei Yin
2021 arXiv   pre-print
To tackle these limitations, we propose a new Social Recommendation framework with Hierarchical Graph Neural Networks (SR-HGNN).  ...  With the revival of deep learning techniques, many efforts have been devoted to developing various neural network-based social recommender systems, such as attention mechanisms and graph-based message  ...  Social Recommendation with Graph Neural Networks.  ... 
arXiv:2110.04039v1 fatcat:txaqxvdtozg4vdqqwitdfncb7u

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [article]

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, Liefeng Bo
2021 arXiv   pre-print
To tackle these challenges, this work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT), to investigate multi-typed interactive patterns between users and items in recommender  ...  Specifically, KHGT is built upon a graph-structured neural architecture to i) capture type-specific behavior characteristics; ii) explicitly discriminate which types of user-item interactions are more  ...  Graph Neural Network Recommender Systems.  ... 
arXiv:2110.04000v1 fatcat:44xhyegydzbmzlf5ytlznzhrqm

A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Rongmei Zhao, Xi Xiong, Xia Zu, Shenggen Ju, Zhongzhi Li, Binyong Li
2020 Complexity  
Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system.  ...  In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only  ...  First, we learn the user representation with user behavior in the item domain by the Context-NE model and then through the hierarchical graph attention model to learn user embedding with social impact  ... 
doi:10.1155/2020/9071624 fatcat:akl2qqzoyrbhbogjhhx7gpbl5a

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation [article]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
2021 arXiv   pre-print
neural networks.  ...  Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.  ...  In other words, each user is influenced recursively by the global social network graph structure.  ... 
arXiv:2104.13030v3 fatcat:7bzwaxcarrgbhe36teik2rhl6e

Recent Advances in Heterogeneous Relation Learning for Recommendation [article]

Chao Huang
2021 arXiv   pre-print
To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation.  ...  We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information.  ...  Graph Neural Network for Social Recommendation. Ad- vances in graph neural networks (GNNs) have shed light on the development of social recommendation solutions  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao, Shirui Pan, Jia Wu, Huaiyu Wan, Huizhi Liang, Haishuai Wang, Huawei Shen
2020 IEEE Access  
His research interests include traffic data mining, social recommender systems, and social network analysis.  ...  The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts.  ... 
doi:10.1109/access.2020.3043060 fatcat:qbqk5f4ojvadlazhk2mc343sra

Recommendation system based on heterogeneous feature: A survey

Hui Wang, ZiChun Le, Xuan Gong
2020 IEEE Access  
This method integrates the user's social relationship into the neural network.  ...  and short-term memory (LSTM), and graph neural network (GNN)-based recommendation methods.  ... 
doi:10.1109/access.2020.3024154 fatcat:clxk77bcr5hdjd3hnxxi6wzlr4

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Areej Bin Suhaim, Jawad Berri
2021 IEEE Access  
Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years.  ...  In this research, we present a comprehensive review of context-aware recommender systems developed for social networks.  ...  The graph-attention neural network proposed in [99] relies on dynamic user's behaviors with recurrent neural network (RNN) and context-dependent social influence to model user's session-based interest  ... 
doi:10.1109/access.2021.3072165 fatcat:i3igbxd44jhrzcyvynevpidcwq

Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

Lianghao Xia, Chao Huang, Yong Xu, Jian Pei
2022 IEEE Transactions on Knowledge and Data Engineering  
The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies.  ...  Towards this end, we propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns, by exploring the evolving  ...  , and O(|E|/S i × L × d) for learning global relation context, where L is the depth of our graph neural network.  ... 
doi:10.1109/tkde.2022.3175094 fatcat:iqreqptfvbeeffmit4isv7xsuu

Hierarchical Context-aware Recurrent Network for Session-based Recommendation

Youfang Leng, Li Yu
2021 IEEE Access  
[20] introduced graph attention networks into the social recommendation for modeling both dynamic user interests and context-dependent social influences. Wu et al.  ...  In this paper, we address this task with a novel recommendation framework, i.e., Hierarchical Context-aware Recurrent Network (HiCAR).  ... 
doi:10.1109/access.2021.3069846 fatcat:5rtu6ppnmjffdbfnp3vojplbdy

Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation [article]

Naicheng Guo, Xiaolei Liu, Shaoshuai Li, Qiongxu Ma, Kaixin Gao, Bing Han, Lin Zheng, Xiaobo Guo
2022 arXiv   pre-print
In this paper, we propose a Poincaré-based heterogeneous graph neural network named PHGR to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios  ...  Then the output of the global representation would be used to complement the local directed item-item homogeneous graph convolution.  ...  HyperSoRec is a novel graph neural network framework that combines hyperbolic learning with social recommendation for social recommendation tasks [45] .  ... 
arXiv:2205.11233v1 fatcat:m42ksn3cjfbwjlsngtdcshzvsm

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
2020 arXiv   pre-print
This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that  ...  filter untruthful noises (e.g., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items.  ...  As one type of graph neural network, Graph Convolutional Network (GCN) has been widely applied in recent social-aware recommendation studies due to the effectiveness in mining social relationships.  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

A Hierarchical Attention Model for Social Contextual Image Recommendation [article]

Le Wu, Lei Chen, Richang Hong, Yanjie Fu, Xing Xie, Meng Wang
2019 arXiv   pre-print
., image visual representation, social network) and user-item historical behavior for enhancing recommendation performance.  ...  Image based social networks are among the most popular social networking services in recent years.  ...  As the social embeddings represent the overall social network with both local and global structure, the improvement is limited with the additional global network structure modeling.  ... 
arXiv:1806.00723v3 fatcat:3jc5twhvyvbevhec5zpzjlhvwy

A Tutorial on Network Embeddings [article]

Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena
2018 arXiv   pre-print
We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.  ...  These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization.  ...  [8] exploit the usage of social listening graph to enhance music recommendation models.  ... 
arXiv:1808.02590v1 fatcat:ramuqdavczfabb4o7r42kice7q

Sequential Recommender Systems: Challenges, Progress and Prospects

Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet Orgun
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.  ...  The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years.  ...  Recently, convolutional neural networks (CNN) and graph neural networks (GNN) have also been applied in SRSs to make up the defects of RNN.  ... 
doi:10.24963/ijcai.2019/883 dblp:conf/ijcai/WangHWCSO19 fatcat:zvzrxzdy7vantpvsdpfrh7mnom
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