We propose a friend recommendation system (an application of link prediction) using edge embeddings on social networks. Most real-world social networks are multi-graphs, where different kinds of relationships (e.g. chat, friendship) are possible between a pair of users. Existing network embedding techniques do not leverage signals from different edge types and thus perform inadequately on link prediction in such networks. We propose a method to mine network representation that effectively exploits heterogeneity in multi-graphs. We evaluate our model on a real-world, active social network where this system is deployed for friend recommendation for millions of users. Our method outperforms various state-of-the-art baselines on Hike's social network in terms of accuracy as well as user satisfaction.
翻译:我们提议了一个朋友建议系统(应用链接预测系统),使用社交网络上的边缘嵌入。大多数真实世界的社交网络是多面网络,在多面网络中,不同类型的关系(如聊天、友谊)是可能的。现有的网络嵌入技术不能利用不同边缘类型的信号,因此在这种网络中的连接预测上表现不力。我们提出了一个有效利用多面网络差异的地雷网络代表方法。我们评估了我们关于现实世界的模型,活跃的社会网络,在现实世界中,这个系统是用来向数百万用户推荐朋友的。我们的方法在准确性和用户满意度方面超过了Hike社交网络上的各种最先进的基线。