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Exploiting Social Review-Enhanced Convolutional Matrix Factorization for Social Recommendation

Xinhua Wang, Xinxin Yang, Lei Guo, Yu Han, Fangai Liu, Baozhong Gao
2019 IEEE Access  
INDEX TERMS Collaborative filtering, convolutional neural network, social network, matrix factorization, social review.  ...  Specifically, to better model the item's reviews, we first introduce the convolutional matrix factorization (ConvMF) as our basic recommendation framework, which utilizes convolutional neural network (  ...  With that in mind, we further incorporate another CNN to SCMF to investigate the effectiveness of user's review context and achieve our final recommendation model Social Review-enhanced Convolutional Matrix  ... 
doi:10.1109/access.2019.2924443 fatcat:gsb4nfpm4feuxbmunn3qphp2du

Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization

Lei Guo, Yu Han, Haoran Jiang, Xinxin Yang, Xinhua Wang, Xiyu Liu
2020 Complexity  
But in reality, as we often turn to our friends for recommendations, the social relationship and social reviews are two important factors that can change our mind most.  ...  To consider user's social influence, we further integrate the user's social network into CMF-I by sharing the user latent factor between user's social network and user-item rating matrix, which can be  ...  Complexity 7 (iv) SocialMF [29] : this is a state-of-the-art social recommendation method that exploits social influence to enhance the recommendation process.  ... 
doi:10.1155/2020/1401236 fatcat:ve72af2syfbwxf6xkdmvnvaftm

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
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.  ...  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  ...  Matrix factorization is probably the most widely used technique for model-based social-aware recommendation. Wen et al.  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

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
After reviewing representative work for each type, we finally discuss some promising directions in this field.  ...  In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers  ...  With the success of TextCNN [89] , a Convolutional Matrix Factorization (ConvMF) is proposed to integrate CNN into probabilistic matrix factorization [82] . Let x i denote the text input of item i.  ... 
arXiv:2104.13030v3 fatcat:7bzwaxcarrgbhe36teik2rhl6e

A Neural Influence Diffusion Model for Social Recommendation [article]

Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang
2019 arXiv   pre-print
In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation.  ...  With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding  ...  Social Recommendation With the prevalence of online social platforms, social recommendation has emerged as a promising direction that leverages the social network among users to enhance recommendation  ... 
arXiv:1904.10322v1 fatcat:hyo2gnvjznejheoc63hojpor3q

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.  ...  Social-aware Factorization Methods. In light of the above challenge, the exploration of social recommendation can date back to the development of social relation-enhanced matrix factorization model.  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

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

Areej Bin Suhaim, Jawad Berri
2021 IEEE Access  
In this research, we present a comprehensive review of context-aware recommender systems developed for social networks.  ...  INDEX TERMS Context-aware system, Contextual factors, Recommender system, Social network Areej Bin-Suhaim received the B.S. degree in information technology from King Saud University (KSU), Riyadh, Saudi  ...  The authors are grateful for this support.  ... 
doi:10.1109/access.2021.3072165 fatcat:i3igbxd44jhrzcyvynevpidcwq

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  
The article by Li et al., ''Matrix factorization for personalized recommendation with implicit feedback and temporal information in social ecommerce networks,'' proposes a smart model named TimeMF by incorporating  ...  multiple information sources into matrix factorization.  ... 
doi:10.1109/access.2020.3043060 fatcat:qbqk5f4ojvadlazhk2mc343sra

A Joint Deep Recommendation Framework for Location-Based Social Networks

Omer Tal, Yang Liu
2019 Complexity  
One is based on convolutional neural networks to extract meaningful data from textual reviews, and the other employs recurrent neural networks.  ...  In addition, we provide further insight into the design selections and hyperparameters of our recommender system, hoping to shed light on the benefit of deep learning for location-based social network  ...  Hierarchical Poisson matrix Factorization. A Bayesian framework for modeling implicit data using Poisson Factorization.  ... 
doi:10.1155/2019/2926749 fatcat:hmdfns6pfrajjkgvm2aj6r3uwu

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks [article]

Zirui Zhu, Chen Gao, Xu Chen, Nian Li, Depeng Jin, Yong Li
2021 arXiv   pre-print
We develop a Social HyperGraph Convolutional Network (short for SHGCN) to learn from the complex triplet social relations.  ...  Incorporating social relations into the recommendation system, i.e. social recommendation, has been widely studied in academic and industrial communities.  ...  This is a famous matrix factorization-based social recommendation model.  ... 
arXiv:2111.03344v1 fatcat:jmqde6idvbc5now3doiz6poch4

Friend Recommendation based on Multi-social Graph Convolutional Network

Liang Chen, Yuanzhen Xie, Zibin Zheng, Huayou Zheng, Jingdun Xie
2020 IEEE Access  
INDEX TERMS Friend recommendation, multi-social graph convolutional network, social network.  ...  Finally, we use Bayesian theory to transform friend recommendation into a sorting problem for personalized recommendation.  ...  Intuitively, learning social networks is helpful for friend The associate editor coordinating the review of this manuscript and approving it for publication was Tallha Akram . recommendation, while the  ... 
doi:10.1109/access.2020.2977407 fatcat:2umf7hnjxbbbhcewpdxxdtujn4

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
Social recommendation which aims to leverage social connections among users to enhance the recommendation performance.  ...  To tackle these limitations, we propose a new Social Recommendation framework with Hierarchical Graph Neural Networks (SR-HGNN).  ...  ACKNOWLEDGMENTS We thank the anonymous reviewers for their constructive feedback and comments. This work is supported by National Nature Science Foundation of China (61672241)  ... 
arXiv:2110.04039v1 fatcat:txaqxvdtozg4vdqqwitdfncb7u

A Heterogeneous Graph Neural Model for Cold-start Recommendation

Siwei Liu, Iadh Ounis, Craig Macdonald, Zaiqiao Meng
2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  
predicted from the social network and textual reviews.  ...  A Heterogeneous Graph Neural Model for Cold-start Recommendation . In  ...  Among all recommendation models, latent factor-based models are the indispensable building blocks of effective recommendations [5, 11] .  ... 
doi:10.1145/3397271.3401252 dblp:conf/sigir/LiuOMM20 fatcat:pcogjke2ivgg5bfbxrbirkunuy

Research Commentary on Recommendations with Side Information: A Survey and Research Directions [article]

Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke
2019 arXiv   pre-print
One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models.  ...  To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree  ...  ACKNOWLEDGEMENTS This work was partly conducted within the Delta-NTU Corporate Lab for Cyber-Physical Systems with funding support from Delta Electronics Inc. and the National Research Foundation (NRF)  ... 
arXiv:1909.12807v2 fatcat:2nj4crzcd5attidhd3kneszmki

Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks

Lei Guo, Haoran Jiang, Xiyu Liu, Changming Xing
2019 Complexity  
As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years.  ...  Many existing studies have focused on how to overcome these challenges by exploiting different types of contexts (e.g., social and geographical information).  ...  [60] integrated the convolutional network into probabilistic matrix factorization model to capture the contextual information of documents and enhance the rating prediction accuracy. Guo et al.  ... 
doi:10.1155/2019/3574194 fatcat:yvdlqwr77jahlovqada6e2zs2e
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