Approximation Algorithms for Budget Splitting in Multi-Channel Influence Maximization

TL;DR

This paper addresses the challenge of optimally splitting advertising budgets across multiple media, introducing a novel influence function and approximation algorithms with proven guarantees, validated by real-world experiments.

Contribution

It introduces a new influence function capturing interaction effects, along with approximation algorithms and theoretical bounds for multi-channel budget allocation.

Findings

Proposed influence function is non-negative, monotone, and non-bisubmodular.

Approximation guarantee achieved by the algorithms is /(1-e^{-b}).

Experiments show improved influence with the proposed budget splitting approach.

Abstract

How to utilize an allocated budget effectively for branding and promotion of a commercial house is an important problem, particularly when multiple advertising media are available. There exist multiple such media, and among them, two popular ones are billboards and social media advertisements. In this context, the question naturally arises: how should a budget be allocated to maximize total influence? Although there is significant literature on the effective use of budgets in individual advertising media, there are hardly any studies examining budget allocation across multiple advertising media. To bridge this gap, this paper introduces the \textsc{Budget Splitting Problem in Billboard and Social Network Advertisement}. We introduce the notion of \emph{interaction effect} to capture the additional influence due to triggers from multiple media of advertising. Using this notion, we propose a noble influence function $\Phi(,)$ that captures the total influence and shows that this function is non-negative, monotone, and non-bisubmodular. We introduce \emph{bi-submodularity ratio $(\gamma)$} and \emph{generalized curvature $(\alpha)$} to measure how close a function is to being bi-submodular and how far a function is from being modular, respectively. We propose the Randomized Greedy and Two-Phase Adaptive Greedy approach, where the influence function is non-bisubmodular and achieves an approximation guarantee of $\frac{1}{\alpha}\left(1-e^ {-\gamma \alpha} \right)$. We conducted several experiments using real-world datasets and observed that the proposed solution approach's budget splitting leads to a greater influence than existing approaches.