计算机科学
推荐系统
水准点(测量)
图形
对偶(语法数字)
协同过滤
背景(考古学)
人工智能
机器学习
社交网络(社会语言学)
代表(政治)
领域(数学分析)
社会化媒体
理论计算机科学
万维网
地理
法学
数学分析
古生物学
艺术
文学类
政治
生物
数学
政治学
大地测量学
作者
Qitian Wu,Hengrui Zhang,Xiaofeng Gao,Peng He,Paul Weng,Han Gao,Guihai Chen
标识
DOI:10.1145/3308558.3313442
摘要
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of constant weights or fixed constraints. To relax this strong assumption, in this paper, we propose dual graph attention networks to collaboratively learn representations for two-fold social effects, where one is modeled by a user-specific attention weight and the other is modeled by a dynamic and context-aware attention weight. We also extend the social effects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that different social effects in two domains could interact with each other and jointly influence user preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social effects. Experiments on one benchmark dataset and a commercial dataset verify the efficacy of the key components in our model. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.
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