  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.
