论文标题
分布式节点涵盖了针对大型网络的优化及其在社交广告上的应用
Distributed Node Covering Optimization for Large Scale Networks and Its Application on Social Advertising
论文作者
论文摘要
组合优化通常是复杂且效率低下的，这限制了其在具有数十亿个链接的大规模网络中的应用。我们引入了一种分布式计算方法，用于在事实场景的规模上解决节点覆盖问题。我们首先构建一种遗传算法，然后设计两步策略来初始化候选解决方案。所有计算操作均在\ textit {apache spark}上以分布式形式进行设计和开发，从而实现实用图的快速计算。我们将我们的方法应用于在线手机游戏中召回回击用户的社交广告，以前仅将其视为传统项目推荐或排名问题。
Combinatorial optimizations are usually complex and inefficient, which limits their applications in large-scale networks with billions of links. We introduce a distributed computational method for solving a node-covering problem at the scale of factual scenarios. We first construct a genetic algorithm and then design a two-step strategy to initialize the candidate solutions. All the computational operations are designed and developed in a distributed form on \textit{Apache Spark} enabling fast calculation for practical graphs. We apply our method to social advertising of recalling back churn users in online mobile games, which was previously only treated as a traditional item recommending or ranking problem.
