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Signal processing techniques for interpolation in graph structured data

Sunil K Narang, Akshay Gadde, Antonio Ortega
2013 2013 IEEE International Conference on Acoustics, Speech and Signal Processing  
We use our proposed method for collaborative filtering in recommendation systems.  ...  We use recent results for sampling in graphs to find classes of bandlimited (BL) graph signals that can be reconstructed from their partially observed samples.  ...  Based on this information, the system predicts new user-movie ratings.  ... 
doi:10.1109/icassp.2013.6638704 dblp:conf/icassp/NarangGO13 fatcat:22h2rewh6raapovrnd5trba6sa

Localized iterative methods for interpolation in graph structured data

Sunil K Narang, Akshay Gadde, Eduard Sanou, Antonio Ortega
2013 2013 IEEE Global Conference on Signal and Information Processing  
In this paper, we present two localized graph filtering based methods for interpolating graph signals defined on the vertices of arbitrary graphs from only a partial set of samples.  ...  The first method is an extension of previous work on reconstructing bandlimited graph signals from partially observed samples.  ...  In Section 4, we describe a second method for graph signal interpolation, based on a regularization framework, in Section 5 we discuss application of proposed method to item-recommendation systems, and  ... 
doi:10.1109/globalsip.2013.6736922 dblp:conf/globalsip/NarangGSO13 fatcat:fmxsao2txvd3betmo4zgodokqi

Localized Iterative Methods for Interpolation in Graph Structured Data [article]

Sunil K. Narang, Akshay Gadde, Eduard Sanou, Antonio Ortega
2013 arXiv   pre-print
In this paper, we present two localized graph filtering based methods for interpolating graph signals defined on the vertices of arbitrary graphs from only a partial set of samples.  ...  The first method is an extension of previous work on reconstructing bandlimited graph signals from partially observed samples.  ...  In Section 4, we describe a second method for graph signal interpolation, based on a regularization framework, in Section 5 we discuss application of proposed method to item-recommendation systems, and  ... 
arXiv:1310.2646v1 fatcat:nx22twny3vhjpn7u6cyjyrf67m

Help Me Find a Job: A Graph-based Approach for Job Recommendation at Scale [article]

Walid Shalaby, BahaaEddin AlAila, Mohammed Korayem, Layla Pournajaf, Khalifeh AlJadda, Shannon Quinn, Wlodek Zadrozny
2018 arXiv   pre-print
In this paper, we propose a scalable item-based recommendation system for online job recommendations.  ...  The short lived nature of the items (jobs) in the system and the rapid rate in which new users and jobs enter the system make the cold-start a serious problem hindering CF methods.  ...  We also would like to thank the consumer email team at CareerBuilder for their help and support to run the large scale A/B test on hundred of thousands of recommendation emails.  ... 
arXiv:1801.00377v1 fatcat:ys6mvsbo6jb7rpmkdew5zba24i

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).  ...  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

Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data

Ander Arriandiaga, Eva Portillo, Josafath Israel Espinosa-Ramos, Nikola K. Kasabov
2019 IEEE Transactions on Neural Networks and Learning Systems  
The proposed method would allow the development of new applications of SNNs as regression techniques for predictive time-series modeling.  ...  The algorithm uses the pulsewidth modulation principles to achieve a high reconstruction accuracy of the signal, along with a high level of data compression.  ...  They would also like to thank Erasmus Mundus Action 2 PANTHER, "Pacific Atlantic Network for Technical High Education and Research" (references: PA/TG1/AUT/ST/08/2015 and PA/TG1/AUT/PhD/02/2015), for the  ... 
doi:10.1109/tnnls.2019.2947380 pmid:31725397 fatcat:zil7mxcqibcc3ofp2nlgixr6qa

Recommendation system based on heterogeneous feature: A survey

Hui Wang, ZiChun Le, Xuan Gong
2020 IEEE Access  
[15] consider matrix completion for recommender systems based on link prediction on graphs.  ...  These methods are introduced, as shown in Figure 2 . 1) Graph semi-supervised learning recommendation system The basic idea of graph-based semi-supervised learning is to construct graphs for all samples  ... 
doi:10.1109/access.2020.3024154 fatcat:clxk77bcr5hdjd3hnxxi6wzlr4

Special Issue on Computational Intelligence Techniques for Industrial and Medical Applications

Gwanggil Jeon, Valerio Bellandi
2020 Concurrency and Computation  
based on graph embedding and diffusion sampling (graph2vec). 2 Their improved model constructs a graph based on users' behavior histories and embeds the graph to a low-dimensional vector space with a  ...  To improve the recommendation accuracy, the authors in the contribution by Chen et al "A novel recommender algorithm based on graph embedding and diffusion sampling" propose a novel recommender algorithm  ... 
doi:10.1002/cpe.5717 fatcat:3xn7riklwrbv7jyh4ug52pbzyy

Self-supervised Graph Learning for Occasional Group Recommendation [article]

Bowen Hao, Hongzhi Yin, Jing Zhang, Cuiping Li, Hong Chen
2022 arXiv   pre-print
Experimental results on three public recommendation datasets show the superiority of our proposed model against the state-of-the-art group recommendation methods.  ...  To further reduce the impact from the cold-start neighbors, we incorporate a self-attention-based meta aggregator to enhance the aggregation ability of each graph convolution step.  ...  After the model is trained, for a new arriving occasional group, based on its first-and high-order neighbors, we can predict an accurate embedding for it.  ... 
arXiv:2112.02274v2 fatcat:c2ejeqzjtfa2hjetvqhuvnyudq

A Simple Graph Convolutional Network with Abundant Interaction for Collaborative Filtering

Ronghui Guo, Xunkai Li, Youpeng Hu, Yixuan Wu, Xin Xiong, Meixia Qu
2021 IEEE Access  
Recently, recommender systems based on Graph Convolution Network (GCN) have become a research hotspot, especially in collaborative filtering.  ...  Comprehensive experiments are conducted on two public datasets, and the results demonstrate that LII-GCCF has a significant improvement over other state-of-the-art methods.  ...  RELATED WORK A. MODEL-BASED CF Collaborative filtering is a recommender systems approach that assumes that users with similar behaviors show similar preferences for items.  ... 
doi:10.1109/access.2021.3083600 fatcat:rk5xzfziavf53bmc67r2iuefke

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  
Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient.  ...  nodes in the network based on the cognitive information, and perform low sampling to achieve relationship prediction.  ...  Based on this, an image recommendation prototype system is designed. is thesis provides two new ideas for the construction of individuallevel knowledge networks, functional procedures, and overall system  ... 
doi:10.1155/2021/5196190 fatcat:dgdd4nxsgbexbo5z4iok4ylr2y

Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge

Haicheng Qu, Jiangtao Guo, Yanji Jiang
2022 Journal of Advanced Transportation  
To mitigate these issues in a deep neural network-based recommendation algorithm, we propose a recommendation algorithm, LG-DropEdge, joint light graph convolutional network, and the DropEdge.  ...  Overfitting in a deep neural network leads to low recommendation precision and high loss.  ...  Acknowledgments is paper was supported by Young Scientists' Fund of National Natural Science Foundation of China and Natural Science Foundation of Liaoning Province.  ... 
doi:10.1155/2022/3843021 doaj:aaf1928cf3874b539af157efce81b530 fatcat:727anlslxnbghnoodhe5git4lu

Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation [article]

Yinghui Tao, Min Gao, Junliang Yu, Zongwei Wang, Qingyu Xiong, Xu Wang
2022 arXiv   pre-print
Based on the finding, we design two auxiliary tasks that are tightly coupled with the target task (one is predictive and the other one is contrastive) towards connecting recommendation with the self-supervision  ...  However, most SSL-based recommendation models rely on general-purpose auxiliary tasks, e.g., maximizing correspondence between node representations learned from the original and perturbed interaction graphs  ...  However, most research on self-supervised learning for recommender systems focuses on homogeneous graphs with a single type of node and edge.  ... 
arXiv:2203.03982v1 fatcat:bgevmwxadnanva666ut24lzonq

Evaluation of objective speech transmission quality measurements in packet-based networks

Oldřich Slavata, Jan Holub
2014 Computer Standards & Interfaces  
This paper presents an analysis of the relation between IP channel characteristics and final voice transmission quality. The NISTNet emulator is used for adjusting the IP channel network.  ...  Jitter and packet loss influence are investigated for the PCM codec and the Speex codec.  ...  They are based on a comparison of the original and transferred sample.  ... 
doi:10.1016/j.csi.2013.09.003 fatcat:nmtbbc657bcm7miex6a44qfaie

Self-Guided Learning to Denoise for Robust Recommendation [article]

Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng
2022 arXiv   pre-print
The experimental results on three benchmark datasets demonstrate the effectiveness of SGDL over the state-of-the-art denoising methods like T-CE, IR, DeCA, and even state-of-the-art robust graph-based  ...  The ubiquity of implicit feedback makes them the default choice to build modern recommender systems.  ...  methods, SGDL has a complexity that is comparable with them, since graph- based methods typically leverage on extra graph structure to en- hance the robustness of the model.  ... 
arXiv:2204.06832v1 fatcat:tajlofkk4vazjevt7jelwo2a7u
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