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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
We focus on the work based on deep learning techniques, an emerging area in the recommendation research.  ...  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  ...  [66] learn user latent representations from the social relationships via deep autoencoders and then use the latent factors in matrix factorization (Figure 1 (d) ). Liu et al.  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation [article]

Yi Tay, Anh Tuan Luu, Siu Cheung Hui
2018 arXiv   pre-print
To the best of our knowledge, our approach is the first data-driven deep learning approach for our novel relationship recommendation problem.  ...  Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users.  ...  . • RQ1 -How well are machine learning and deep learning methods able to learn, predict, recommend relationships just based on linguistic information from social profiles?  ... 
arXiv:1805.11535v1 fatcat:yyctnvrkrvewnc7km2i7tdegdi

Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

Liangtian Wan, Feng Xia, Xiangjie Kong, Ching-Hsien Hsu, Runhe Huang, Jianhua Ma
2020 IEEE Transactions on Network Science and Engineering  
To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence.  ...  Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied.  ...  In conclusion, the main contributions of this paper are listed as follows: 1) The deep learning techniques are integrated with the social trust relationship to improve the recommendation performance. 2  ... 
doi:10.1109/tnse.2020.3044035 fatcat:jfrirag34zcuvdjyda7tqo556i

A Survey of Deep Learning Approaches for Recommendation Systems

Jun Yi Liu
2018 Journal of Physics, Conference Series  
This paper provides a survey of recommendation systems, which focuses on deep learning approaches and the system of applications.  ...  As deep learning develops, the application of deep neural network in related research is increasingly prevalent.  ...  Yu et al. introduced an image privacy protection system by identifying sensitive objects via deep multi-task learning. Deng et al. introduced trust-aware recommendations in social networks.  ... 
doi:10.1088/1742-6596/1087/6/062022 fatcat:2mep7pcyvrdvldwq7z3ts6w6gm

Neural Personalized Ranking via Poisson Factor Model for Item Recommendation

Yonghong Yu, Li Zhang, Can Wang, Rong Gao, Weibin Zhao, Jing Jiang
2019 Complexity  
Experimental results on two real-world datasets show that our proposed method compares favorably with the state-of-the-art recommendation algorithms.  ...  Some work has been proposed to support the personalized recommendation by utilizing collaborative filtering to learn the latent user and item representations from implicit interactions between users and  ...  [38] proposed the deep collaborative filtering framework (DCF), which unifies the deep learning models with MF based CF.  ... 
doi:10.1155/2019/3563674 fatcat:rc4kaow6fzg5dpppucsjdcewsy

An Attention-Based Friend Recommendation Model in Social Network

Chongchao Cai, Huahu Xu, Jie Wan, Baiqing Zhou, Xiongwei Xie
2020 Computers Materials & Continua  
Then, we obtained user preferences by using the relationships between users and items, which were later inputted into our model to learn the preferences between friends.  ...  Existing systems make recommendations mainly according to users' preferences with a particular focus on items.  ...  In social networks, different friends have different influences on users with the same attention relationship.  ... 
doi:10.32604/cmc.2020.011693 fatcat:hsxm7uzgozamzl22tla4u2n3wa

Friend Recommendation based on Multi-social Graph Convolutional Network

Liang Chen, Yuanzhen Xie, Zibin Zheng, Huayou Zheng, Jingdun Xie
2020 IEEE Access  
This paper focuses on integrating various social relationships to guide the representation learning, and further generating personalized friend recommendations.  ...  Friend recommendations based on social relationships have attracted thousands of research under the rapid development of social networks.  ...  Third, it can also be combined with explainable recommendation system to strengthen the interpretability of the deep learning model of friend recommendation.  ... 
doi:10.1109/access.2020.2977407 fatcat:2umf7hnjxbbbhcewpdxxdtujn4

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-learning Approaches

Sadaf Safavi, Mehrdad Jalali, Mahboobeh Houshmand
2022 Electronics  
it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses.  ...  New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than  ...  [162] suggested a method for POI recommendation employing deep learning in LBSNs with respect to privacy. First, user information, relationships, and location information are reviewed.  ... 
doi:10.3390/electronics11131998 fatcat:exuhjcsn3rbw5d3xjsw3aykmhe

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
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  ...  To tackle these limitations, we propose a new Social Recommendation framework with Hierarchical Graph Neural Networks (SR-HGNN).  ...  With the prevalence of social networks in real-life online applications [12] , [24] , a key line of research work seeks to boost the recommendation performance via exploiting the users' social relationships  ... 
arXiv:2110.04039v1 fatcat:txaqxvdtozg4vdqqwitdfncb7u

Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks

Shaohui Du, Zhenghan Chen, Haoyan Wu, Yihong Tang, YuanQing Li, Huihua Chen
2021 Complexity  
Therefore, this article mainly studies image recommendation algorithms based on deep neural networks in social networks.  ...  Then, some feature vectors are created via traditional feature algorithms like LBP, BGC3, RTU, or CNN extraction.  ...  In the analysis of social network, the centrality of a node is the number of other nodes that establish a relationship with the node.  ... 
doi:10.1155/2021/5196190 fatcat:dgdd4nxsgbexbo5z4iok4ylr2y

Image Recommendation With Reciprocal Social Influence

Yuan Meng, Chunyan Han, Yongfeng Zhang, Yanjie Li, Guibing Guo
2019 IEEE Access  
Social relationships have been exploited to learn user preference, and shown their effectiveness.  ...  In this paper, we propose a deep neural network for image recommendation (dubbed RSIM) by leveraging reciprocal social influence, and optimize the preferences of users and friends simultaneously.  ...  IMAGE RECOMMENDATION Recently, deep learning techniques have been widely used to learn embedding representation of images, which are beneficial for downstream applications, such as recommendation, image  ... 
doi:10.1109/access.2019.2939403 fatcat:loklgbbntnexphzgmwfpnv5ave

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

Academic Collaborator Recommendation Based on Attributed Network Embedding

Ouxia Du, Ya Li
2022 Journal of Data and Information Science  
collaborator recommendation tasks.  ...  A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space. Findings 1.  ...  Deep autoencoder is a deep neural network model for feature learning, which can learn highly non-linear topological and attribute features.  ... 
doi:10.2478/jdis-2022-0005 fatcat:ln7ndhkv2ncqdjiqkxgqcjfw74

Deep Contextual Learning for Event-Based Potential User Recommendation in Online Social Networks

T. Manojpraphakar, A. Soundarrajan
2022 Intelligent Automation and Soft Computing  
Event recommendation allows people to identify various recent upcoming social events.  ...  The proposed prototype model Correlation Aware Deep Contextual Learning (CADCL) solves the mentioned issues.  ...  In Section 3, the correlation aware deep influence prediction framework for recommending profile to the social event using Deep belief Network.  ... 
doi:10.32604/iasc.2022.025090 fatcat:iy3vh2yilbgndm4znef4yk4mgm

DSINE: Deep Structural Influence Learning via Network Embedding

Jianjun Wu, Ying Sha, Bo Jiang, Jianlong Tan
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To this end, we propose a deep structural influence learning model to learn social influence structure via mining rich features of each user, and fuse information from the aligned selfnetwork component  ...  Structural representations of user social influence are critical for a variety of applications such as viral marketing and recommendation products.  ...  Introduction The use of relation information in social networks plays an important role in recommending products, promoting public opinions and detecting information anomalies.  ... 
doi:10.1609/aaai.v33i01.330110065 fatcat:33ym4kowpzgtlcquwggiyst324
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