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When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation [article]

Yu Tian and Jianxin Chang and Yannan Niu and Yang Song and Chenliang Li
2022 arXiv   pre-print
Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history.  ...  and SURGE elect to refine user preferences based on multi-level correlations between historical items.  ...  Then, the emergence of neural networks further enhances recommender systems' ability to extracte the preference of users, so another paradigm of sequence recommendation method based on neural networks  ... 
arXiv:2205.01286v1 fatcat:wlct2okdyrfbtjvllpb6f5fhmi

Dynamic Graph Neural Networks for Sequential Recommendation [article]

Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang
2021 arXiv   pre-print
Furthermore, we design a Dynamic Graph Recommendation Network to extract user's preferences from the dynamic graph.  ...  We take inspiration from dynamic graph neural networks to cope with this challenge, modeling the user sequence and dynamic collaborative signals into one framework.  ...  scenario. • We propose DGSR, a new sequential recommendation framework based on dynamic graph neural networks. • We conduct empirical studies on three real-world datasets.  ... 
arXiv:2104.07368v2 fatcat:74kljncl4nfefeehuqjg6g47du

ISCC 2020 Keyword Index

2020 2020 IEEE Symposium on Computers and Communications (ISCC)  
algorithm Genetic Algorithm Glaucoma Gossip GPU Grammatical Evolution Graph Convolutional Network Graph Neural Networks grey model group key management guaranteed based approach hardware/software  ...  neural network Convolutional Neural Network convolutional neural networks Convolutional Neural Networks Cooperative Management Coronavirus Cortex-A9 MPCore Cost function modification Cost-Efficiency  ... 
doi:10.1109/iscc50000.2020.9219679 fatcat:al6gjafwwneo5g5paprquzp7n4

IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data

Ruocheng Guo, Jundong Li, Yichuan Li, K. Selçuk Candan, Adrienne Raglin, Huan Liu
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
We propose IGNITE that learns representations of confounders from networked observational data, which is trained by a minimax game to achieve the two desiderata.  ...  Existing methods show the potential of utilizing network information to handle confounding bias, but they only try to satisfy one of the two desiderata.  ...  Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Special Track on AI in FinTech  ... 
doi:10.24963/ijcai.2020/618 dblp:conf/ijcai/ChengWZ020 fatcat:kmocgyioivbh5alguamwibopxu

Layer-refined Graph Convolutional Networks for Recommendation [article]

Xin Zhou, Donghui Lin, Yong Liu, Chunyan Miao
2022 arXiv   pre-print
Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological structure of the user-item  ...  interaction graph.  ...  Since GCN [4] was first outlined to generalize convolutional neural networks(CNNs) on graph-structured data, GCN and its variants have been successfully applied to solve various recommendation problems  ... 
arXiv:2207.11088v1 fatcat:xpxz76iisfgv3nmksmkjloedte

Editorial for the special issue on "Research on methods of multimodal information fusion in emotion recognition"

Kaijian Xia, Tao Hu, Wen Si
2019 Personal and Ubiquitous Computing  
convolutional neural network (CNN) is proposed.  ...  The paper "Emotional computing based on cross-modal fusion and edge network data incentive" presented an emotional computing algorithm based on cross-modal fusion and edge network data incentive.  ...  In the paper "Dynamic social privacy protection based on graph mode partition in complex social network", dynamic social privacy protection based on graph pattern partitioning is designed to satisfy differential  ... 
doi:10.1007/s00779-019-01260-x fatcat:6e7btyoj7zbtlahdghv23qkrye

SelfCF: A Simple Framework for Self-supervised Collaborative Filtering [article]

Xin Zhou, Aixin Sun, Yong Liu, Jie Zhang, Chunyan Miao
2022 arXiv   pre-print
We propose and study three output perturbation techniques that can be applied to different types of backbone networks including both traditional CF models and graph-based models.  ...  The proposed SelfCF framework simplifies the Siamese networks and can be easily applied to existing deep-learning based CF models, which we refer to as backbone networks.  ...  With the emerge of graph convolutional networks (GCNs), which generalize convolutional neural networks (CNNs) on graph-structured data, GCN-based CF is widely researched recently [1, 44, 47] .  ... 
arXiv:2107.03019v2 fatcat:gwuduuttdrgdhhwd4atbtrbmvi

Spectral collaborative filtering

Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, Philip S. Yu
2018 Proceedings of the 12th ACM Conference on Recommender Systems - RecSys '18  
Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users' experiences with Recommender Systems  ...  To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets.  ...  Inspired by the recent success of graph/node embedding methods, [2] proposes a graph convolution network based model for recommendations.  ... 
doi:10.1145/3240323.3240343 dblp:conf/recsys/ZhengLJZY18 fatcat:z57ox6claffa7pjzquggbn7ilu

Hyperbolic Hypergraphs for Sequential Recommendation [article]

Yicong Li, Hongxu Chen, Xiangguo Sun, Zhenchao Sun, Lin Li, Lizhen Cui, Philip S. Yu, Guandong Xu
2021 arXiv   pre-print
To alleviate the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based hypergraph convolutional neural network to learn the dynamic item embeddings.  ...  However, compared with traditional graph-based methods, the constructed hypergraphs are usually much sparser, which leads to a dilemma when balancing the benefits of hypergraphs and the modelling difficulty  ...  Hyperbolic Space-based Hypergraph Convolutional Network.  ... 
arXiv:2108.08134v1 fatcat:zifmaxbgovdffazhmeyop447y4

Table of Contents

2022 IEEE Transactions on Cybernetics  
Lü 2110 Probabilistic Graph Convolutional Network via Topology-Constrained Latent Space Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  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  ... 
doi:10.1109/tcyb.2022.3162412 fatcat:2kilcb3oq5bqnfvm5q7whvm5le

Table of contents

2020 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS)  
Neural Network 038-Y069 Dynamic Gesture Recognition Based on 3D Separable Convolutional LSTM Networks 180 Xunlei Zhang,Yun Tie, Lin Qi 039-Y042 Bandit based Dynamic Spectrum Anti-jamming Strategy in Software  ...  -D127 Aspect Based Sentiment Analysis with Self-Attention and Gated Convolutional Networks 146 Jian Yang, Juan Yang Session 4 031-D128 Attention Network for Group Recommendation 150 Qingwei Chen  ... 
doi:10.1109/icsess49938.2020.9237648 fatcat:a2e6ptkqlvehlcwzjzitqgytr4

Tensor graph convolutional neural network [article]

Tong Zhang, Yang Li (1 and 2) the Department of Information Science and Engineering, Southeast University, Nanjing, China the Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China the School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China)
2018 arXiv   pre-print
In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic  ...  Encapsuling sequence graphs into a recursive learning, the dynamics of graphs can be efficiently encoded as well as the spatial layout of graphs.  ...  Further more, recent year, based on spectral graph theory, graph convolution neural network (GCNN) is proposed in [4] , [9] , [12] for irregular data as an alternative algorithm to CNN and shows promising  ... 
arXiv:1803.10071v1 fatcat:ziquzxmllbeo5hhf5l34mkixby

Modeling User Behavior with Graph Convolution for Personalized Product Search [article]

Fan Lu, Qimai Li, Bo Liu, Xiao-Ming Wu, Xiaotong Zhang, Fuyu Lv, Guli Lin, Sen Li, Taiwei Jin, Keping Yang
2022 arXiv   pre-print
To capture implicit user preference signals and collaborative patterns, we use an efficient jumping graph convolution to explore high-order relations to enrich product representations for user preference  ...  In this work, we propose to address the limitations of prior arts by exploring local and global user behavior patterns on a user successive behavior graph, which is constructed by utilizing short-term  ...  Information Retrieval with Graphs Graph-based methods have been extensively explored in the literature of sequential recommendation.  ... 
arXiv:2202.06081v1 fatcat:dwqkmpsqmrhkrpqatcbxa5eqja


2020 2020 IEEE International Conference on Knowledge Graph (ICKG)  
211 Deqing Yang (Fudan University), Zikai Guo (Fudan University), and Yanghua Xiao (Fudan University) Scientific Workflow Recommendation Based on Service Knowledge Graph 219 Jin Diao (China University  ...  Shu Zhao (Anhui university), and Yanping Zhang (Anhui University) Heterogeneous Dynamic Graph Attention Network 404 Qiuyan Li (Beijing University of Posts and Telecommunications, China), Yanlei Shang  ... 
doi:10.1109/icbk50248.2020.00004 fatcat:6qeywzs5offfrfycjlbusmslpa

Table of contents

2022 IEEE Transactions on Systems, Man & Cybernetics. Systems  
Behera 101 Manufacturing Systems and Smart Industry Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series . . . C. Yin, S. Zhang, J. Wang, and N. N.  ...  Panneerselvam 166 Risk-Aware and Collision-Preventive Cooperative Fleet Cruise Control Based on Vehicular Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tsmc.2021.3132711 fatcat:tjd6lehqz5et7hycr4m57tofia
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