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Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks [chapter]

Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin
<span title="2021-08-31">2021</span> <i title="IOS Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qv2vo27d4zghvkz2dhzcboly5e" style="color: black;">Applications and Practices in Ontology Design, Extraction, and Reasoning</a> </i> &nbsp;
Naturally, we represent such a scenario as a temporal knowledge graph and compare plain knowledge graph, a taxonomy and a hypergraph embedding approach, as well as a recurrent neural network architecture  ...  Learned latent vector representations are key to the success of many recommender systems in recent years.  ...  We attempt that by a hypergraph-and a taxonomy embedding technique and recurrent neural networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/ssw210046">doi:10.3233/ssw210046</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rfsad4zo7zhybdjloyor4zjczu">fatcat:rfsad4zo7zhybdjloyor4zjczu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210907094714/https://ebooks.iospress.nl/pdf/doi/10.3233/SSW210046" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b9/30/b93063d222688998e12d235144d61afffb03c64f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/ssw210046"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Titlepage

<span title="">2019</span> <i title="IEEE"> 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC) </i> &nbsp;
18 Personalized Recommendation Method of POI Based on Deep Neural Network 19 TPM: A GPS-based Trajectory Pattern Mining System 20 The Dynamic Factors of Guangdong's Economic Growth from the  ...  Approach by Fusing Images based on Neural Networks 32 Deep neural network-based classification model for Sentiment Analysis 33 Micro-blog User Profiling: A Supervised Clustering based Approach  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/besc48373.2019.8963202">doi:10.1109/besc48373.2019.8963202</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6vphrvvwpbac3frzixtnyi7g4u">fatcat:6vphrvvwpbac3frzixtnyi7g4u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429034029/https://ieeexplore.ieee.org/ielx7/8952729/8962976/08963202.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4c/55/4c5573e67e592f70d0b4bee584899aac8768ffab.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/besc48373.2019.8963202"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Hyperbolic Hypergraphs for Sequential Recommendation [article]

Yicong Li, Hongxu Chen, Xiangguo Sun, Zhenchao Sun, Lin Li, Lizhen Cui, Philip S. Yu, Guandong Xu
<span title="2021-08-18">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender system.  ...  To alleviate the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based hypergraph convolutional neural network to learn the dynamic item embeddings.  ...  RELATED WORK 5.1 Non-graph sequential modelling for neural recommendation Early neural recommendations building on typical deep neural networks mostly use recurrent neural networks (RNN) or convolutional  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.08134v1">arXiv:2108.08134v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zifmaxbgovdffazhmeyop447y4">fatcat:zifmaxbgovdffazhmeyop447y4</a> </span>
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Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, Xiangliang Zhang
<span title="2021-04-19">2021</span> <i title="ACM"> Proceedings of the Web Conference 2021 </i> &nbsp;
Hypergraph provides a natural way to model high-order relations, while its potentials for improving social recommendation are under-explored.  ...  To compensate for the aggregating loss, we innovatively integrate self-supervised learning into the training of the hypergraph convolutional network to regain the connectivity information with hierarchical  ...  As for the applications in social recommendation, HMF [62] uses hypergraph topology to describe and analyze the interior relation of social network in recommender systems, but it does not fully exploit  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3442381.3449844">doi:10.1145/3442381.3449844</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wmmkxp5rnzc6henb5lnmih7f4m">fatcat:wmmkxp5rnzc6henb5lnmih7f4m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210716224708/https://repository.kaust.edu.sa/bitstream/handle/10754/669805/3442381.3449844.pdf;jsessionid=DD180ED1A9504A79E5A8B02D837534A9?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c2/8a/c28a1077dbd4107aafa562e00a6c56cb64d24772.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3442381.3449844"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation [article]

Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, Xiangliang Zhang
<span title="2022-02-27">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems.  ...  Hypergraph provides a natural way to model complex high-order relations, while its potentials for improving social recommendation are under-explored.  ...  Multi-Channel Hypergraph Convolutional Network for Social Recommendation In this section, we present our model MHCN, which stands for Multi-channel Hypergraph Convolutional Network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.06448v4">arXiv:2101.06448v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dnjuxs7xgjaahod2qfetzj6fsa">fatcat:dnjuxs7xgjaahod2qfetzj6fsa</a> </span>
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IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search [article]

Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He
<span title="2022-02-10">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
On this basis, we develop a specific interactive hypergraph neural network to explicitly encode the structure information (i.e., collaborative signal) into the embedding process.  ...  A good personalized product search (PPS) system should not only focus on retrieving relevant products, but also consider user personalized preference.  ...  Being aware of the effectiveness of graph neural network (GNN) for relational representation learning [15, 20] , we wish to take its advantages for PPS.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.04972v1">arXiv:2202.04972v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5qwtw3sa4zfl3kjtvnjn6utshy">fatcat:5qwtw3sa4zfl3kjtvnjn6utshy</a> </span>
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Hypergraph Pre-training with Graph Neural Networks [article]

Boxin Du, Changhe Yuan, Robert Barton, Tal Neiman, Hanghang Tong
<span title="2021-05-23">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address this issue, this paper presents an end-to-end, bi-level pre-training strategy with Graph Neural Networks for hypergraphs.  ...  Among others, a major hurdle for effective hypergraph representation learning lies in the label scarcity of nodes and/or hyperedges.  ...  [34] propose a multi-channel hypergraph convolutional network for social recommendation. Other recent neural hyperlink prediction methods include [30] [31] [32] [33] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.10862v1">arXiv:2105.10862v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m6lairp6mbgoph4g7fcgamq4lm">fatcat:m6lairp6mbgoph4g7fcgamq4lm</a> </span>
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Graph Neural Networks in Recommender Systems: A Survey

Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui
<span title="2022-05-05">2022</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eiea26iqqjcatatlgxdpzt637y" style="color: black;">ACM Computing Surveys</a> </i> &nbsp;
Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority  ...  Due to the important application value of recommender systems, there have always been emerging works in this field.  ...  neural network, which encodes high-order data correlation in a hypergraph structure.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3535101">doi:10.1145/3535101</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hgv2tbx3k5hzbnkupwsysqwjmy">fatcat:hgv2tbx3k5hzbnkupwsysqwjmy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220506204142/https://dl.acm.org/doi/pdf/10.1145/3535101" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ca/94/ca94471a095334eae06ee57170c5943d0b2893ca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3535101"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction [article]

Zhonghang Li and Chao Huang and Lianghao Xia and Yong Xu and Jian Pei
<span title="2022-05-07">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.  ...  Specifically, we propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space.  ...  ACKNOWLEDGMENTS We thank the anonymous reviewers for their constructive feedback and comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.08587v2">arXiv:2204.08587v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fdjwlnuqjfhjxhpwxj5r7sysva">fatcat:fdjwlnuqjfhjxhpwxj5r7sysva</a> </span>
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Self-supervised Recommendation with Cross-channel Matching Representation and Hierarchical Contrastive Learning [article]

Dongjie Zhu, Yundong Sun, Haiwen Du, Zhaoshuo Tian
<span title="2021-09-14">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This is the first attempt in the field of recommender systems, we believe the insight of this paper is inspirational to future self-supervised learning research based on multi-channel information.  ...  Compared with the traditional graph neural network, it can better model the multi-channel interactive data of the social network. Some GNN-based methods for recommender systems are proposed recently.  ...  To explore the high-order interactions and semantic interaction patterns in the recommender systems, some recent studies have begun to combine hypergraph neural networks with selfsupervised learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.00676v3">arXiv:2109.00676v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ateh67wf7bcbxkhsevmyvlwd2m">fatcat:ateh67wf7bcbxkhsevmyvlwd2m</a> </span>
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Graph Neural Networks in Recommender Systems: A Survey [article]

Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui
<span title="2022-04-02">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority  ...  Due to the important application value of recommender systems, there have always been emerging works in this field.  ...  neural network, which encodes high-order data correlation in a hypergraph structure.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.02260v4">arXiv:2011.02260v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hvk22yyid5bzjnzmzchyti25ja">fatcat:hvk22yyid5bzjnzmzchyti25ja</a> </span>
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Fair Multi-Stakeholder News Recommender System with Hypergraph ranking [article]

Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos
<span title="2021-02-09">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recommender systems are typically designed to fulfill end user needs. However, in some domains the users are not the only stakeholders in the system.  ...  Most of the collaborative filtering recommender systems suffer from popularity bias.  ...  To leverage this information, the CNN based deep neural network approach proposed by [32] is used to generate article embeddings for news articles of this dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.00387v2">arXiv:2012.00387v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zroyahcjevgank2wb2o6ecsup4">fatcat:zroyahcjevgank2wb2o6ecsup4</a> </span>
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You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks [article]

Eli Chien, Chao Pan, Jianhao Peng, Olgica Milenkovic
<span title="2022-03-28">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose AllSet, a new hypergraph neural network paradigm that represents a highly general framework for (hyper)graph neural networks and for the first time implements hypergraph neural network layers  ...  To enable efficient processing of hypergraph-structured data, several hypergraph neural network platforms have been proposed for learning hypergraph properties and structure, with a special focus on node  ...  Pan Li at Purdue University for helpful discussions. The authors would also like to thank Chaoqi Yang for answering questions regarding the LEGCN method.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.13264v4">arXiv:2106.13264v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ir6f3d7bwnhc7o5dqvxpnjxgfq">fatcat:ir6f3d7bwnhc7o5dqvxpnjxgfq</a> </span>
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Graph Neural Networks Designed for Different Graph Types: A Survey [article]

Josephine M. Thomas and Alice Moallemy-Oureh and Silvia Beddar-Wiesing and Clara Holzhüter
<span title="2022-04-06">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Based on this, the young research field of Graph Neural Networks (GNNs) has emerged.  ...  Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems.  ...  Acknowledgment The GAIN-project is funded by the Ministry of Education and Research Germany (BMBF), under the funding code 01IS20047A, according to the 'Policy for the funding of female junior researchers  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.03080v1">arXiv:2204.03080v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vkbytocc3bfm3cky52j7zlmuyy">fatcat:vkbytocc3bfm3cky52j7zlmuyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220516032254/https://arxiv.org/pdf/2204.03080v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c6/10/c6108ba033f008307c0cdbe028e913798219be5e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.03080v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

2019 Index IEEE Transactions on Knowledge and Data Engineering Vol. 31

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ht3yl6qfebhwrg7vrxkz4gxv3q" style="color: black;">IEEE Transactions on Knowledge and Data Engineering</a> </i> &nbsp;
Wang, Y., +, TKDE July 2019 1327-1340 Knowledge based systems Collective Keyword Query on a Spatial Knowledge Base.  ...  Shi, L., +, TKDE June 2019 1094-1108 Differentially Private Mixture of Generative Neural Networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tkde.2019.2953412">doi:10.1109/tkde.2019.2953412</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jkmpnsjcf5a3bhhf4ian66mj5y">fatcat:jkmpnsjcf5a3bhhf4ian66mj5y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106144444/https://ieeexplore.ieee.org/ielx7/69/8926561/08926562.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/80/1e/801e0a04a357deedc73a882fae4f9053bba14883.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tkde.2019.2953412"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>
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