4.6 Article

Enhancing signed social recommendation via extracting consistent and inconsistent relations

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation

Le Wu et al.

Summary: Influenced by the success of deep learning, research in recommendation has shifted to developing new recommender models based on neural networks. This survey paper systematically reviews neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize the field and facilitate researchers and practitioners. It categorizes the work into collaborative filtering, content enriched recommendation, and temporal/sequential recommendation based on the data usage, and discusses promising directions in the field.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

Article Computer Science, Artificial Intelligence

Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities

Edwin Puertas et al.

Summary: The study explores how language variations in digital social networks help detect communities, leading to a better understanding of their dynamics and social foundations for improved communication policies and interventions when needed.

COGNITIVE COMPUTATION (2021)

Proceedings Paper Computer Science, Information Systems

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

Liangwei Yang et al.

Summary: Social recommendation addresses the cold-start problem in rating prediction by leveraging Graph Neural Networks (GNNs). Despite this advancement, the issue of social inconsistency is often overlooked, leading to the proposal of a method that samples consistent neighbors and utilizes relation attention mechanisms to tackle this problem effectively. Experiments on real-world datasets validate the effectiveness of the model.

SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (2021)

Proceedings Paper Computer Science, Information Systems

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

Xiangnan He et al.

PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20) (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Deep Social Collaborative Filtering

Wenqi Fan et al.

RECSYS 2019: 13TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (2019)

Proceedings Paper Computer Science, Theory & Methods

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

Qitian Wu et al.

WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) (2019)

Proceedings Paper Computer Science, Theory & Methods

Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

Yixin Cao et al.

WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) (2019)

Proceedings Paper Computer Science, Theory & Methods

Graph Neural Networks for Social Recommendation

Wenqi Fan et al.

WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) (2019)

Proceedings Paper Computer Science, Information Systems

Neural Graph Collaborative Filtering

Xiang Wang et al.

PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19) (2019)

Proceedings Paper Computer Science, Information Systems

A Neural Influence Diffusion Model for Social Recommendation

Le Wu et al.

PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19) (2019)

Article Computer Science, Information Systems

A novel social network hybrid recommender system based on hypergraph topologic structure

Xiaoyao Zheng et al.

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

Learning node and edge embeddings for signed networks

Wenzhuo Song et al.

NEUROCOMPUTING (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Signed Graph Convolutional Networks

Tyler Derr et al.

2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Spectral Collaborative Filtering

Lei Zheng et al.

12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS) (2018)

Proceedings Paper Computer Science, Information Systems

Recommender Systems with Characterized Social Regularization

Tzu-Heng Lin et al.

CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Rex Ying et al.

KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)

Article Computer Science, Artificial Intelligence

Social Collaborative Filtering by Trust

Bo Yang et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Proceedings Paper Computer Science, Information Systems

Neural Collaborative Filtering

Xiangnan He et al.

PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

TDRec: Enhancing Social Recommendation using Both Trust and Distrust Information

Tiansheng Bai et al.

SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015) (2015)

Review Computer Science, Information Systems

Social recommendation: a review

Jiliang Tang et al.

SOCIAL NETWORK ANALYSIS AND MINING (2013)

Article Multidisciplinary Sciences

Social selection and peer influence in an online social network

Kevin Lewis et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2012)

Article Computer Science, Artificial Intelligence

Trust- and Distrust-Based Recommendations for Controversial Reviews

Patricia Victor et al.

IEEE INTELLIGENT SYSTEMS (2011)

Article Computer Science, Hardware & Architecture

MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS

Yehuda Koren et al.

COMPUTER (2009)