A Regret-Aware Framework for Effective Social Media Advertising

TL;DR

A noble regret model is proposed that captures the aggregated loss encountered by the influence provider while allocating the seed nodes while allocating the seed nodes in the social media Advertisement problem.

Abstract

Social Media Advertisement has emerged as an effective approach for promoting the brands of a commercial house. Hence, many of them have started using this medium to maximize the influence among the users and create a customer base. In recent times, several companies have emerged as Influence Provider who provides views of advertisement content depending on the budget provided by the commercial house. In this process, the influence provider tries to exploit the information diffusion phenomenon of a social network, and a limited number of highly influential users are chosen and activated initially. Due to diffusion phenomenon, the hope is that the advertisement content will reach a large number of people. Now, consider that a group of advertisers is approaching an influence provider with their respective budget and influence demand. Now, for any advertiser, if the influence provider provides more or less influence, it will be a loss for the influence provider. It is an important problem from the point of view of influence provider, as it is important to allocate the seed nodes to the advertisers so that the loss is minimized. In this paper, we study this problem, which we formally referred to as Regret Minimization in Social Media Advertisement Problem. We propose a noble regret model that captures the aggregated loss encountered by the influence provider while allocating the seed nodes. We have shown that this problem is a computationally hard problem to solve. We have proposed three efficient heuristic solutions to solve our problem, analyzed to understand their time and space requirements. They have been implemented with real world social network datasets, and several experiments have been conducted and compared to many baseline methods.

Figures (17)

• Figure 1: A schematic diagram showing the allocation of seed nodes to advertisers in a social network.
• Figure 2: Regret of varying the demand-supply ratio $\lambda$ when $\omega = 10\%$ (Email-Eu-Core, $|\mathcal{A}| = 100$, $p_{c}: Uniform(0.1)$)
• Figure 7: Regret of varying the demand-supply ratio $\lambda$ when $\omega = 10\%$ (Email-Eu-Core, $|\mathcal{A}| = 100$, $p_{c}: Trivalency$)
• Figure 12: Regret of varying the demand-supply ratio $\lambda$ when $\omega = 10\%$ (Facebook, $|\mathcal{A}| = 100$, $p_{c}: Uniform(0.1)$)
• Figure 17: Regret of varying the demand-supply ratio $\lambda$ when $\omega = 10\%$ (Facebook, $|\mathcal{A}| = 100$, $p_{c}: Trivalency$)

Theorems & Definitions (11)

• Definition 1: Independent Cascade Model
• Definition 2: Influence Maximization Problem (cost version)
• Theorem 1
• Definition 3: The Regret Model
• Definition 4: Feasible Selection
• Definition 5: Regret Associated with a Selection
• Definition 6: Regret Minimization in Social Media Advertisement Problem
• Definition 7: Marginal Decrease in Regret
• Theorem 2
• Theorem 3