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.

翻译：我们考虑社会广告的收入最大化问题,即社会网络平台所有者需要为一组广告商选择种子用户,每个广告商都有支付预算,从而最大限度地实现广告所有者通过在网络中宣传广告从广告商获得的预期收入总额。以前关于该问题的研究表明,这一问题是棘手的,并呈现近似算法。我们从新的角度重新审视这一问题,并开发新的高效近似算法,既在假定确切影响或触角的环境下,又在这种假设宽松的环境下。我们的近似比率比以往显著提高。此外,我们通过对四个数据集的广泛实验,经验显示我们的算法在解决方案质量和计算效率两方面都大大优于现有方法。