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Exploiting Social Review-Enhanced Convolutional Matrix Factorization for Social Recommendation
2019
IEEE Access
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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
2020
Complexity
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2021
IEEE Access
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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
2020
IEEE Access
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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
2019
Complexity
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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]
2021
arXiv
pre-print
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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
2020
IEEE Access
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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]
2021
arXiv
pre-print
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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
2020
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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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]
2019
arXiv
pre-print
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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
2019
Complexity
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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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