Multi-product Influence Maximization in Billboard Advertisement

2025-10-10

AI Summary

This paper studies the multi-product billboard influence maximization problem: jointly selecting billboards under limited advertising space to satisfy the influence requirements of multiple products. It formalizes two novel problem variants: (1) selecting $k$ billboards such that each product’s influence meets a specified threshold; and (2) partitioning billboards into $ell$ pairwise disjoint subsets (each of size at most $k_i$) to collectively satisfy all product requirements. For the first time, these are modeled as multi-submodular coverage and its generalized disjoint variant—extending beyond classical single-product submodular optimization. For Problem (1), we design a bi-criteria approximation algorithm; for Problem (2), we propose an efficient sampling-based approximation algorithm. Experiments on real-world trajectory and billboard datasets demonstrate that our methods significantly improve multi-product coverage and demand satisfaction rates, achieving both theoretical approximation guarantees and practical efficiency.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

Billboard Advertisement has emerged as an effective out-of-home advertisement technique where the goal is to select a limited number of slots and play advertisement content over there with the hope that this will be observed by many people, and effectively, a significant number of them will be influenced towards the brand. Given a trajectory and a billboard database and a positive integer $k$, how can we select $k$ highly influential slots to maximize influence? In this paper, we study a variant of this problem where a commercial house wants to make a promotion of multiple products, and there is an influence demand for each product. We have studied two variants of the problem. In the first variant, our goal is to select $k$ slots such that the respective influence demand of each product is satisfied. In the other variant of the problem, we are given with $ell$ integers $k_1,k_2, ldots, k_{ell}$, the goal here is to search for $ell$ many set of slots $S_1, S_2, ldots, S_{ell}$ such that for all $i in [ell]$, $|S_{i}| leq k_i$ and for all $i eq j$, $S_i cap S_j=emptyset$ and the influence demand of each of the products gets satisfied. We model the first variant of the problem as a multi-submodular cover problem and the second variant as its generalization. For solving the first variant, we adopt the bi-criteria approximation algorithm, and for the other variant, we propose a sampling-based approximation algorithm. Extensive experiments with real-world trajectory and billboard datasets highlight the effectiveness and efficiency of the proposed solution approach.

Problem

• Selecting limited billboard slots to maximize multi-product advertisement influence
• Satisfying specific influence demands for multiple products simultaneously
• Solving two variants of multi-product influence maximization with approximation algorithms

Innovation

• Multi-submodular cover modeling for influence maximization
• Bi-criteria approximation algorithm for slot selection
• Sampling-based approximation for disjoint slot allocation