Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.
翻译:Facebook和Twitter等在线社群非常受欢迎,并已成为许多用户日常生活的重要组成部分。 通过这些平台,用户可以发现和创建其他人随后会消费的信息。在这方面,向用户推荐相关信息对可行性至关重要。然而,在线社群的建议是一个具有挑战性的问题:(1) 用户的利益是动态的,(2) 用户受到其朋友的影响。此外,影响者可能受背景影响。也就是说,在不同主题上可能依赖不同的朋友。因此,两个信号的建模对于提出建议至关重要。我们建议在线社群采用一个基于动态绘图感应神经网络的推荐系统。我们用一个经常性神经网络来模拟动态用户行为,用一个图形感应神经网络来模拟根据具体情况产生的社会影响,这些网络根据用户当前利益动态地推断出影响者。整个模型可以有效地适应大型数据。几个真实世界数据集的实验结果表明我们所提议的方法在几个竞争性基线上的有效性,包括状态型模型。