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Enhancing Graph Neural Networks for Recommender Systems

Siwei Liu
2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  
Recommender systems lie at the heart of many online services such as E-commerce, social media platforms and advertising.  ...  To keep users engaged and satisfied with the displayed items, recommender systems usually use the users' historical interactions  ...  Our proposed methods and techniques aim to alleviate those limitations and help the effective deployment of GNNs in recommender systems. KEYWORDS Recommender Systems, Graph Neural Networks  ... 
doi:10.1145/3397271.3401456 dblp:conf/sigir/Liu20 fatcat:7jaijmct5zcnpceu2yfi3mif5a

Rule-Guided Graph Neural Networks for Recommender Systems [article]

Xinze Lyu and Guangyao Li and Jiacheng Huang and Wei Hu
2020 arXiv   pre-print
In this paper, we propose RGRec, which combines rule learning and graph neural networks (GNNs) for recommendation.  ...  To alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resources.  ...  In this paper, we design RGRec, a method integrating automatic rule learning and graph neural network (GNN)-based aggregation for recommendation.  ... 
arXiv:2009.04104v1 fatcat:sprbznk7vvczhdvdzz3semwtme

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems [article]

Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang
2019 arXiv   pre-print
Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.  ...  As such knowledge graphs represent an attractive source of information that could help improve recommender systems.  ...  Therefore, we present a Knowledge Graph Convolutional Neural Network (KGCN) approach for recommender systems.  ... 
arXiv:1905.04413v3 fatcat:myn3gx7yfzaanlpyblonoynqf4

Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems [article]

Weibin Li, Mingkai He, Zhengjie Huang, Xianming Wang, Shikun Feng, Weiyue Su, Yu Sun
2021 arXiv   pre-print
In recent years, owing to the outstanding performance in graph representation learning, graph neural network (GNN) techniques have gained considerable interests in many real-world scenarios, such as recommender  ...  systems and social networks.  ...  Recently, as Graph Neural Networks (GNNs) have shown their capability of modeling complex structural data, many existing works attempt to adopt GNNs for representation learning and recommender systems.  ... 
arXiv:2112.01035v2 fatcat:2wjlfc75xfh7zlus3swh25sqf4

SceneRec: Scene-Based Graph Neural Networks for Recommender Systems [article]

Gang Wang, Ziyi Guo, Xiang Li, Dawei Yin, Shuai Ma
2021 arXiv   pre-print
In the scene-based graph, we adopt graph neural networks to learn scene-specific representation on each item node, which is further aggregated with latent representation learned from collaborative interactions  ...  Collaborative filtering has been largely used to advance modern recommender systems to predict user preference.  ...  It leverages graph neural networks to propagate scene information and learn the scene-specific representation for each item.  ... 
arXiv:2102.06401v1 fatcat:sztel6ajtnbqxfcagovcqb76bm

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec
2018 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18  
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks.  ...  To our knowledge, this is the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.  ...  Acknowledgments The authors acknowledge Raymond Hsu, Andrei Curelea and Ali Altaf for performing various A/B tests in production system, Jerry Zitao Liu for providing data used by Pixie [14] , and Vitaliy  ... 
doi:10.1145/3219819.3219890 dblp:conf/kdd/YingHCEHL18 fatcat:xp5aezpyjbcvfdjjmke3ivjgm4

Detecting Beneficial Feature Interactions for Recommender Systems [article]

Yixin Su, Rui Zhang, Sarah Erfani, Zhenghua Xu
2021 arXiv   pre-print
Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features.  ...  To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that  ...  neural networks.  ... 
arXiv:2008.00404v6 fatcat:en3fjgcpznekzjfqr7n7bafp7i

Learning to Hash with Graph Neural Networks for Recommender Systems [article]

Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu
2020 arXiv   pre-print
In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly  ...  Despite its effectiveness in learning embedding vectors for objects in the user-item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous  ...  ACKNOWLEDGMENTS The authors thank the anonymous reviewers for their helpful comments. The work is in part supported by NSF IIS-1718840, IIS-1750074, and IIS-1900990.  ... 
arXiv:2003.01917v1 fatcat:wpexdptpeze7tdumqb6r4lhn7u

Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks

Ruihong Qiu, Zi Huang, Jingjing Li, Hongzhi Yin
2020 ACM Transactions on Information Systems (TOIS; Formerly: ACM Transactions on Office Information Systems)  
In this paper, we solve these problems with the graph neural networks technique.  ...  First, each session is represented as a graph rather than a linear sequence structure, based on which a novel Full Graph Neural Network (FGNN) is proposed to learn complicated item dependency.  ...  At last, we will describe graph neural networks (GNN) for the node representation learning and graph classification problems in Section 2.3.  ... 
doi:10.1145/3382764 fatcat:byzvhh4eenbebg7f7qdhr7wrme

Graph Convolutional Neural Network for a Pharmacy Cross-Selling Recommender System

Franz Hell, Yasser Taha, Gereon Hinz, Sabine Heibei, Harald Müller, Alois Knoll
2020 Information  
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance in recommender system benchmarks.  ...  Via modifications to the sampling involved in the network optimization process, we address a common phenomenon in recommender systems, the so-called popularity bias: popular products are frequently recommended  ...  neural networks to graph-structured data.  ... 
doi:10.3390/info11110525 fatcat:rwazxjyobnhh5m6s5upr7k57d4

Inductive Matrix Completion Based on Graph Neural Networks [article]

Muhan Zhang, Yixin Chen
2020 arXiv   pre-print
patterns around a (user, item) pair are effective predictors of the rating this user gives to the item; and 3) Long-range dependencies might not be necessary for modeling recommender systems.  ...  IGMC trains a graph neural network (GNN) based purely on 1-hop subgraphs around (user, item) pairs generated from the rating matrix and maps these subgraphs to their corresponding ratings.  ...  RELATED WORK Graph neural networks Graph neural networks (GNNs) are a new type of neural networks for learning over graphs (Scarselli et al., 2009; Bruna et al., 2013; Duvenaud et al., 2015; Li et al.  ... 
arXiv:1904.12058v3 fatcat:bodp3t4hencl5fjck4ykj5yflu

GReS: Workshop on Graph Neural Networks for Recommendation and Search

Thibaut Thonet, Stéphane Clinchant, Carlos Lassance, Elvin Isufi, Jiaqi Ma, Yutong Xie, Jean-Michel Renders, Michael Bronstein
2021 Fifteenth ACM Conference on Recommender Systems  
The GReS workshop on Graph Neural Networks for Recommendation and Search is then a first endeavor to bridge the gap between the RecSys and GNN communities, and promote recommendation and search problems  ...  Graph neural networks (GNNs) have recently gained significant momentum in the recommendation community, demonstrating state-of-the-art performance in top-k recommendation and next-item recommendation.  ...  The GReS workshop on Graph Neural Networks for Recommendation and Search is then a first endeavor to bridge the gap between these two communities and foster inter-collaborations, creating a more attractive  ... 
doi:10.1145/3460231.3470937 fatcat:i4ixqrqmwbfxdlukairprjg3ni

Attributed Graph Neural Networks for Recommendation Systems on Large-Scale and Sparse Graph [article]

Yifei Zhao, Mingdong Ou, Rongzhi Zhang, Meng Li
Link prediction in structured-data is an important problem for many applications, especially for recommendation systems.  ...  In this paper, we propose a practical solution about graph neural networks called Attributed Graph Convolutional Networks(AGCN) to incorporate edge attributes when apply graph neural networks in large-scale  ...  Graph Neural Networks Graph Neural Networks(GNN), which can capture both graph structure and nodes' attributes in the network, have shown its superiority in link prediction task (Kipf and Welling 2016  ... 
doi:10.48550/arxiv.2112.13389 fatcat:uxjdutllhrazdky62q7dzmbmk4

Using Graph Neural Networks for Program Termination [article]

Yoav Alon, Cristina David
2022 arXiv   pre-print
Compared to previous approaches using neural networks for program termination, we also take advantage of the graph representation of programs by employing Graph Neural Networks.  ...  Overall, we designed and implemented classifiers for program termination based on Graph Convolutional Networks and Graph Attention Networks, as well as a semantic segmentation Graph Neural Network that  ...  In particular, as explained next, we use Graph Neural Networks (GNNs) [66] and Graph Attention Networks (GANs) [61] .  ... 
arXiv:2207.14648v1 fatcat:nqnggy5rhjcqzngggguqwmqfsy

A review of recommendation system research based on bipartite graph

Ziteng Wu, Chengyun Song, Yunqing Chen, Lingxuan Li, I. Barukčić
2021 MATEC Web of Conferences  
An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop  ...  Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system.  ...  The third generation is a recommendation system based on graph neural networks with continuous achievements in recent years.  ... 
doi:10.1051/matecconf/202133605010 fatcat:qyiiqjnev5eublqlmps4gcworq
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