Pricing Social Visibility Service in Online Social Networks: Modeling and Algorithms

TL;DR

This paper models and develops algorithms for pricing a social visibility boosting service in online social networks, aiming to maximize revenue through optimal pricing and supplier selection under budget constraints.

Contribution

It introduces a novel pricing mechanism for social visibility services, formulates the revenue maximization problem, proves its computational hardness, and provides an efficient approximation algorithm with theoretical guarantees.

Findings

Proposed a posted pricing scheme for social visibility boosting.

Proved the revenue maximization problem is NP-hard.

Developed an approximation algorithm with provable performance guarantees.

Abstract

Online social networks (OSNs) such as YoutTube, Instagram, Twitter, Facebook, etc., serve as important platforms for users to share their information or content to friends or followers. Oftentimes, users want to enhance their social visibility, as it can make their contents, i.e., opinions, videos, pictures, etc., attract attention from more users, which in turn may bring higher commercial benefit to them. Motivated by this, we propose a mechanism, where the OSN operator provides a "social visibility boosting service" to incentivize "transactions" between requesters (users who seek to enhance their social visibility via adding new "neighbors") and suppliers (users who are willing to be added as a new "neighbor" of any requester when certain "rewards" is provided). We design a posted pricing scheme for the OSN provider to charge the requesters who use such boosting service, and reward the suppliers who contribute to such boosting service. The OSN operator keeps a fraction of the payment from requesters and distributes the remaining part to participating suppliers "fairly" via the Shapley value. The objective of the OSN provider is to select the price and supplier set to maximize the total amount of revenue under the budget constraint of requesters. We first show that the revenue maximization problem is not simpler than an NP-hard problem. We then decomposed it into two sub-routines, where one focuses on selecting the optimal set of suppliers, and the other one focuses on selecting the optimal price. We prove the hardness of each sub-routine, and eventually design a computationally efficient approximation algorithm to solve the revenue maximization problem with provable theoretical guarantee on the revenue gap. We conduct extensive experiments on four public datasets to validate the superior performance of our proposed algorithms.