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2022
IEEE Transactions on Cybernetics
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Ding 2505 (Contents Continued on Page 1978) (Contents Continued from Page 1977)
∞ (Contents Continued from Front Cover) Intralayer Synchronization of Multiplex Dynamical Networks via Pinning Impulsive ...
Lü 2110 Probabilistic Graph Convolutional Network via Topology-Constrained Latent Space Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tcyb.2022.3162412
fatcat:2kilcb3oq5bqnfvm5q7whvm5le
Recent Advances in Heterogeneous Relation Learning for Recommendation
[article]
2021
arXiv
pre-print
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We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information. ...
To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation. ...
(i) Graph Convolutional Network-based Models. Inspired General GNN Social Recommendation Framework. ...
arXiv:2110.03455v1
fatcat:fskj4qdsibfnxefklazdli3tgu
Table of Contents
2021
IEEE transactions on multimedia
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Hou Multimedia Search and Retrieval Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Jung Multimedia for Immersive Search Space and Personalized Recommendations Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks . . . . . . . . . . ...
doi:10.1109/tmm.2021.3132246
fatcat:el7u2udtybddrpbl5gxkvfricy
User Cold-start Recommendation via Inductive Heterogeneous Graph Neural Network
2022
ACM Transactions on Information Systems
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To tackle this limitation, this paper proposes a novel Inductive Heterogeneous Graph Neural Network (IHGNN) model, which utilizes the relational information in user cold-start recommendation systems to ...
Our model converts new users, items, associated multimodal information into a Modality-aware Heterogeneous Graph (M-HG), which preserves the rich and heterogeneous relationship information among them. ...
methods across all position parameters, indicating the User Cold-start Recommendation via Inductive Heterogeneous Graph Neural Network • 21
Fig. 6 . 6 Fig. 6. ...
doi:10.1145/3560487
fatcat:at4e5mfq6vgujbfaiijo22yvtq
Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
[article]
2023
arXiv
pre-print
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We try to review recent literature on graph neural network-based recommender systems, covering the background and development of both recommender systems and graph neural networks. ...
Then categorizing recommender systems by their settings and graph neural networks by spectral and spatial models, we explore the motivation behind incorporating graph neural networks into recommender systems ...
Graph Convolution Networks A notable advancement in recent years is the incorporation of graph convolutional networks (GCN) [13, 34, 35, 36, 37, 38] into recommendation systems. ...
arXiv:2311.06323v1
fatcat:rc5tkn5fajh6lp6ztx54norrzq
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
[article]
2020
arXiv
pre-print
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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 ...
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. ...
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
Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation
2019
Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19
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Learning high quality node embeddings is of key interest, and Graph Convolutional Networks (GCNs) have recently been established as the latest state-of-the-art methods in representation learning. ...
It makes up current GCN's limitation in modelling heterogeneous relations with a relation-aware aggregator, and leverages the semantic-aware meta-paths to carve out concise and relevant receptive fields ...
Motivated by the limitations of current GCN models, we design a novel Relation-aware Co-attentive Graph Convolutional Networks (RecoGCN) for representation based recommendation on HINs. ...
doi:10.1145/3357384.3357924
dblp:conf/cikm/XuLHLX019
fatcat:wgznfbekajeg5kezix2dgjeecm
CAME: Content- and Context-Aware Music Embedding for Recommendation
2020
IEEE Transactions on Neural Networks and Learning Systems
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Specifically, a heterogeneous information network (HIN) is first presented to incorporate different kinds of content and context data. ...
Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content- and context-aware music recommendation method. ...
CAME can learn the content-and context-aware embeddings of music pieces via network embedding and convolutional neural networks (CNNs) with attention mechanism and is able to model the intrinsic features ...
doi:10.1109/tnnls.2020.2984665
pmid:32305946
fatcat:ody4ay2swfepvhmofyvgv36n6q
Table of Contents
2021
2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
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655 Knowledge Graph Construction and Decision Support Towards Transformer Fault Maintenance Xiaoying Liu and Hongwei Wang 661 Dynamic design of the turbomachinery blade with the joint application of the ...
Prediction via Dynamic Graph Neural Networks Zongmai Cao, Kai Han and Jianfu Zhu First-order and High-order Information Fusion over Heterogeneous Information Network for Top-N Recommendation System Nan ...
Graph through Malicious HTTP Requests Shengqin Ao, Yitong He, Ning Luo, Xuren Wang, Zhengwei Jiang and Jun Jiang Attributed Heterogeneous Graph Neural Network for Malicious Domain Detection Shuai Zhang ...
doi:10.1109/cscwd49262.2021.9437777
fatcat:z6wycq4v4nb7fcsoxh7pmmmesa
Disentangled Graph Social Recommendation
[article]
2023
arXiv
pre-print
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In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user ...
Learning the disentangled representations with relation heterogeneity poses great challenge for social recommendation. ...
Then, it perform intent-aware message passing over graph convolutional network for recommendation. • DisenHAN [47] : This model is built over the graph attention model to encode the disentangled embeddings ...
arXiv:2303.07810v1
fatcat:tgoq4switjfsfm5x5okzofivni
Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey
[article]
2024
arXiv
pre-print
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This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems, addressing the lack of depth and precision in existing literature. ...
Also, the paper illuminates the future prospects of big data techniques in recommendation systems, underscoring potential advancements and opportunities for further research in this field ...
Uber's Context-Aware Restaurant Recommendations in Uber Eats Friend Recommendations via Graph Neural Networks (GNNs) in social media [126] : Social networks like Facebook could use GNNs to suggest friends ...
arXiv:2402.03368v1
fatcat:alwsy4cpljhyvj5rdvygzhda44
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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Dutta, T., +, TMM 2021 2833-2842 Heterogeneous Community Question Answering via Social-Aware Multi-Modal Co-Attention Convolutional Matching. ...
Wang, X., +, TMM 2021 692-705 Expression-Aware Face Reconstruction via a Dual-Stream Network. Chai, X., +, TMM 2021 2998-3012 Dynamic Point Cloud Inpainting via Spatial-Temporal Graph Learning. ...
., Low-Rank Pairwise Align- ment Bilinear Network For Few-Shot Fine-Grained Image Classification; TMM 2021 1666-1680 Huang, H., see 1855 -1867 Huang, H., see Jiang, X., TMM 2021 2602-2613 Huang, J., ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
Graph Neural Networks Designed for Different Graph Types: A Survey
[article]
2022
arXiv
pre-print
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To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. ...
We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. ...
, blockchain knowledge graph KGIN [81] link prediction recommender systems content-associated heterogeneous HetG [97] link prediction, recommendation, node clustering (inductive) node classification, review ...
arXiv:2204.03080v4
fatcat:smc5pmimzfdmdbx5n53eaovjny
Personalized News Recommendation: Methods and Challenges
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2106.08934v2 fatcat:mpbs3vr75ncw7npnp5uhwj5v4eOther Versions
To help researchers master the advances in personalized news recommendation over the past years, in this paper we present a comprehensive overview of personalized news recommendation. ...
Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and ...
GNUD [56] uses a disentangled graph convolution network to learn user representations from the user-news graph. ...
arXiv:2106.08934v3
fatcat:iagqsw73hrehxaxpvpydvtr26m
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation
[article]
2021
arXiv
pre-print
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We use graph convolutions to learn the multi-hop relationship among the entities of the same type and leverage a self-attention-based hypergraph neural network to learn the ternary relationships among ...
the interacting entities via hyperlink prediction in a 3-uniform hypergraph. ...
Consistency-aware and Attention-based Recommender (CAR) [2] introduces the global preferences of users and a consistency-aware gating mechanism to capture heterogeneity in item consistency for list continuation ...
arXiv:2110.01467v2
fatcat:todwosunifbjlb5z5lm2tkttia
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