Discount allocation for cost minimization in online social networks

We introduce the discount allocation problem to a new online social networks (OSNs) scenario where the nodes and the relationships between nodes are determined but the states of edges between nodes are unknown. We can know the states of all the edges centered on a node only when it becomes active. Different from most previous work on influence maximization discount allocation problem in OSNs, our goal is to minimize the discount cost that the marketer spends while ensuring at least Q customers who adopt the target product in the end in OSNs. We propose an online discount allocation policy to select seed users to spread the product information. The marketer initially selects one seed user to offer him a discount and observes whether he accepts the discount. If he accepts the discount, the marketer needs to observe how well this seed user contributes to the diffusion of product adoptions and how much discount he accepts. The remaining seeds are chosen based on the feedback of diffusion results obtained by all previous selected seeds. We propose two online discount allocation greedy algorithms under two different situations: uniform and non-uniform discounts allocation. We offer selected users discounts changing from the lowest to highest in the discount rate set until the users receive the discount and become seed users in non-uniform discount allocation situation, which saves the cost of firms comparing with the previous method that providing product to users for free. We present a theoretical analysis with bounded approximation ratios for the algorithms. Extensive experiments are conducted to evaluate the performance of the proposed online discount allocation algorithms on real-world online social networks datasets and the results demonstrate the effectiveness and efficiency of our methods.

Automated summary

This paper introduces a discount allocation problem in online social networks, aiming to minimize marketing costs while ensuring a certain number of customers adopt the target product. Two greedy algorithms are proposed and extensive experiments demonstrate the effectiveness and efficiency of the methods.