TY - GEN
T1 - DCAT
T2 - International Conference on Advances in Mobile
Computing & Multimedia (17th : 2019)
AU - Ghafari, Seyed Mohssen
AU - Joshi, Aditya
AU - Beheshti, Amin
AU - Paris, Cecile
AU - Yakhchi, Shahpar
AU - Orgun, Mehmet
PY - 2019
Y1 - 2019
N2 - Customer reviews are now increasingly available on Online Social Networks (OSNs) for a wide range of products and services. Trust in the review's author is a crucial basis for believing in the reliability of reviews generated on such networks. In this context, the main challenge is to predict the unknown trust relationship between two users. Existing trust prediction approaches fail to incorporate textual footprint of users. To address this challenge, we present a deep learning-based graph analytics model to predict trust relations in OSNs. We leverage and extend GraphSAGE, a method for computing node representations in an inductive manner, to develop a deep classifier. We present our experiment with datasets from review websites to train classifiers that predict trust relations between pairs of users, and highlight how our approach significantly improves the quality of predicted trust relations compared to the state-of-the-art approaches.
AB - Customer reviews are now increasingly available on Online Social Networks (OSNs) for a wide range of products and services. Trust in the review's author is a crucial basis for believing in the reliability of reviews generated on such networks. In this context, the main challenge is to predict the unknown trust relationship between two users. Existing trust prediction approaches fail to incorporate textual footprint of users. To address this challenge, we present a deep learning-based graph analytics model to predict trust relations in OSNs. We leverage and extend GraphSAGE, a method for computing node representations in an inductive manner, to develop a deep classifier. We present our experiment with datasets from review websites to train classifiers that predict trust relations between pairs of users, and highlight how our approach significantly improves the quality of predicted trust relations compared to the state-of-the-art approaches.
KW - Trust Prediction
KW - Context-Aware
KW - Online Social Networks
KW - User Embeddings
KW - Deep Learning
KW - Graph Convolutional Networks
UR - http://www.scopus.com/inward/record.url?scp=85090280597&partnerID=8YFLogxK
U2 - 10.1145/3365921.3365940
DO - 10.1145/3365921.3365940
M3 - Conference proceeding contribution
T3 - ACM International Conference Proceeding Series
SP - 20
EP - 27
BT - Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia (MoMM2019)
A2 - Haghighi, Pari Delir
A2 - Salvadori, Ivan Luiz
A2 - Steinbauer, Matthias
A2 - Khalil, Ismail
A2 - Anderst-Kotsis, Gabriele
PB - Association for Computing Machinery (ACM)
CY - New York, NY
Y2 - 2 December 2019 through 4 December 2019
ER -