论文标题
大规模的朋友建议建筑
A Large-scale Friend Suggestion Architecture
论文作者
论文摘要
在线社交作为传统生活的扩展，在我们的日常生活中起着重要作用。用户经常寻找具有重要相似之处的新朋友，例如兴趣和习惯，激励我们利用此类在线信息向用户建议朋友。在这项工作中，我们专注于在线游戏平台中的朋友建议，因为游戏中的社交质量与玩家的参与度有很大相关，从而确定了游戏体验。与取决于项目用户交互的典型推荐系统不同，在我们的设置中，用户 - 用户交互并不取决于彼此。同时，由于快速变化的游戏环境，用户偏好迅速变化。在面对这些困难时，几乎没有工作在设计朋友建议方面，这是我们第一次打算在大规模的在线游戏中解决这个问题。在快速变化的在线游戏环境中，我们通过建模用户之间的相似性演变来提出这个问题，以利用用户在游戏中的长期和短期特征。我们在大型游戏数据集上与数百万用户进行的实验表明，我们所提出的模型比其他竞争基线实现了卓越的性能。
Online social as an extension of traditional life plays an important role in our daily lives. Users often seek out new friends that have significant similarities such as interests and habits, motivating us to exploit such online information to suggest friends to users. In this work, we focus on friend suggestion in online game platforms because in-game social quality significantly correlates with player engagement, determining game experience. Unlike a typical recommendation system that depends on item-user interactions, in our setting, user-user interactions do not depend on each other. Meanwhile, user preferences change rapidly due to fast changing game environment. There has been little work on designing friend suggestion when facing these difficulties, and for the first time we aim to tackle this in large scale online games. Motivated by the fast changing online game environment, we formulate this problem as friend ranking by modeling the evolution of similarity among users, exploiting the long-term and short-term feature of users in games. Our experiments on large-scale game datasets with several million users demonstrate that our proposed model achieves superior performance over other competing baselines.
