Open AccessPA-GAN: Graph Attention Network for Preference-Aware Social Recommendation
Author(s) -
Liyang Hou,
Wenping Kong,
Yali Gao,
Yang Chen,
Xiaoyong Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012141
Subject(s) - preference , computer science , graph , recommender system , representation (politics) , focus (optics) , attention network , information retrieval , social network (sociolinguistics) , social media , artificial intelligence , theoretical computer science , world wide web , mathematics , statistics , physics , optics , politics , political science , law
Social recommendation has been recently proposed by incorporating trust relationship to alleviate data-sparsity and cold-start problems. However, most of existing works only focus on friends different contribution to model user representation. They ignore users have different preference on items, and share different preference with friends. To address these problems, in this paper, we propose a novel Preference-Aware Graph Attention Network (PA-GAN) for trust recommendation. And we design three modules: item aggregation for user, friend-preference aggregation for user and user aggregation for item to model users’ local and global preference. Experiments on two publicly available datasets shows the proposed model PA-GAN outperforms the state-of-the-art recommendation models, and improves performance greatly.