MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation

Xiao, Yang; Yao, Lina; Pei, Qingqi; Wang, Xianzhi; Yang, Jian; Sheng, Quan Z.

doi:10.1109/mis.2020.2988925

Cited by 16 publications

(4 citation statements)

References 19 publications

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“…No contexto de abordagem não sequencial, Xiao et al (2020) utilizam os grafos Usuário-Usuário e Usuário-Item para prever relacionamentos de amizade entre indivíduos (link prediction) e a probabilidade de aquisição de produtos/itens no contexto de redes sociais. Para isso, os autores consideram que usuários que são amigos em uma rede social têm propensão a consumir os mesmos produtos, ao mesmo tempo que indivíduos que têm hábitos de consumo similares tendem a se tornarem amigos.…”

Section: Sistemas De Recomendaçãounclassified

“…A i j = 1 se v i e v j estabelecem algum relacionamento ou contato e A i j = 0 caso contrário (Xiao et al, 2020), (Tomy et al, 2022), (Mondal et al, 2020), (Sanchez et al, 2021) e (Sanchez et al, 2021) Frequência A i j indica a frequência de transições/viagens entre v i e v j (Capanema et al, 2021b) e (La Gatta et al, 2020) Similaridade A i j representa o grau de similaridade entre as representações de v i e v j (Shen et al, 2018) Vizinhança A i j = 1 se v i e v j forem vizinhos e A i j = 0 caso contrário. Esse é um caso particular de relacionamento/contato para entidades geográficas (S. Hu et al, 2021) e (S. [G (1) ,G (2) , .…”

Section: Relacionamento/ Contatounclassified

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Redes Neurais de Grafos no Contexto das Cidades Inteligentes

Capanema¹,

Silva²,

Loureiro³

2022

Minicursos Do XL Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos

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Graph neural networks, also known as GNNs, have been applied to solve problems in different domains, such as biology, chemistry, physics, natural language processing, computer vision, economics, among others. In particular, this class of neural network has been shown to be effective in modeling problems related to smart cities, such as traffic prediction; urban function classification of elements such as points of interest, roads and regions; forecasting the spread of diseases; autonomous agents; anomaly detection, among other activities. The objective of this chapter is to present the theoretical and practical foundations of graph neural networks in the context of smart cities. For this, the following topics are presented: types of tasks performed, taxonomy of GNNs layers, concepts, commonly used architectures, problems related to smart cities, modeling for the transformation of raw data to the graph structure and GNN structure, in addition to the implementation a model in practice. ResumoRedes neurais de grafos, também conhecidas como Graph Neural Networks (GNNs), têm sido aplicadas para resolver problemas em diferentes domínios, como biologia, química, física, processamento de linguagem natural, visão computacional, economia, dentre outros. Em particular, essa classe de rede neural tem-se mostrado eficaz na modelagem de problemas relacionados às cidades inteligentes, como predição de tráfego; classificação de função urbana de elementos como pontos de interesse, estradas e regiões; previsão de disseminação de doenças; agentes autônomos; detecção de anomalia, dentre outras atividades. O objetivo deste capítulo é apresentar os fundamentos teóricos e práticos das redes neurais de grafos no contexto das cidades inteligentes. Para isto, são apresentados os seguintes tópicos: tipos de tarefas realizadas, taxonomia das camadas GNNs, conceitos, arquiteturas comumente utilizadas, problemas relacionados

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Section: Sistemas De Recomendaçãounclassified

Section: Relacionamento/ Contatounclassified

Redes Neurais de Grafos no Contexto das Cidades Inteligentes

Capanema¹,

Silva²,

Loureiro³

2022

Minicursos Do XL Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos

View full text Add to dashboard Cite

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“…DSCF [8] includes high-order social links through a sequential learning on random walks. MGNN [37] builds mutual social embedding layers to aggregate information from user-item rating graph and social graph.…”

mentioning

confidence: 99%

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

Yang

Liu

Dou

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Social recommendation aims to fuse social links with user-item interactions to alleviate the cold-start problem for rating prediction. Recent developments of Graph Neural Networks (GNNs) motivate endeavors to design GNN-based social recommendation frameworks to aggregate both social and user-item interaction information simultaneously. However, most existing methods neglect the social inconsistency problem, which intuitively suggests that social links are not necessarily consistent with the rating prediction process. Social inconsistency can be observed from both contextlevel and relation-level. Therefore, we intend to empower the GNN model with the ability to tackle the social inconsistency problem. We propose to sample consistent neighbors by relating sampling probability with consistency scores between neighbors. Besides, we employ the relation attention mechanism to assign consistent relations with high importance factors for aggregation. Experiments on two real-world datasets verify the model effectiveness.

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“…The first article entitled "MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation" by Xiao et al 4 proposes a holistic approach to predict users' preferences on friends and items jointly and thus generates more accurate recommendations on both friends and items for the users. This enables the modeling of users' consumption preferences and social influence simultaneously.…”

mentioning

confidence: 99%

The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning

Wang

Pasi

et al. 2020

IEEE Intell. Syst.

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Sydney & IT IS OUR pleasure to share with you this special issue on intelligent recommendation with advanced artificial intelligence (AI) and learning, which includes eight articles published in the September/October issue of IEEE Intelligent Systems (IS). After our announcement in early August 2019 for this special issue, we received 40 submissions, only 8 ones out of which are accepted to be included in this special issue. After a long period of hard-work of review from anonymous reviewers and careful revisions from the authors, we finally achieve the current versions of the accepted papers published in this issue. First, we would like to sincerely thank all the contributors and the anonymous reviewers for their great efforts made on this special issue. Second, we express our sincere gratitude to the IS team led by Professor Venkatramanan Subrahmanian for their support and help to ensure the timely publication of this issue.

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MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation

Cited by 16 publications

References 19 publications

Redes Neurais de Grafos no Contexto das Cidades Inteligentes

Redes Neurais de Grafos no Contexto das Cidades Inteligentes

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning

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