社会化广告中收益最大化的高效算法
Efficient and Effective Algorithms for Revenue Maximization in Social Advertising
摘要：
我们考虑的收入最大化问题，在社交网络平台的所有者需要选择种子用户的一组广告客户，每个支付预算，使所有者通过传播他们的广告在网络中从广告客户获得的总预期收入最大化。以往的研究表明，这是一个棘手的问题，并提出了近似算法。我们重新审视这个问题，从一个新的角度来看，并开发新的高效的近似算法，无论是在设置一个确切的影响甲骨文假设，并在此假设是放松。我们的近似比显着改善了以前的。此外，我们经验表明，使用广泛的实验在四个数据集上，我们的算法大大优于现有的方法的解决方案的质量和计算效率。
英文摘要：
We consider the revenue maximization problem in social advertising, where a social network platform owner needs to select seed users for a group of advertisers, each with a payment budget, such that the total expected revenue that the owner gains from the advertisers by propagating their ads in the network is maximized. Previous studies on this problem show that it is intractable and present approximation algorithms. We revisit this problem from a fresh perspective and develop novel efficient approximation algorithms, both under the setting where an exact influence oracle is assumed and under one where this assumption is relaxed. Our approximation ratios significantly improve upon the previous ones. Furthermore, we empirically show, using extensive experiments on four datasets, that our algorithms considerably outperform the existing methods on both the solution quality and computation efficiency.
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