Trajectory-Driven Multi-Product Influence Maximization in Billboard Advertising

2026-01-21

AI Summary

This work addresses the multi-product influence maximization problem in trajectory-driven outdoor advertising by introducing two practical settings—shared and mutually exclusive ad slots—formulated as multi-submodular cover and its generalized variant. For the first time, it integrates real-world trajectory data with multi-product influence requirements to construct a constrained multi-submodular cover model. The authors design an algorithmic framework that balances approximation guarantees and feasibility, combining continuous greedy, randomized rounding, sampling-based approximation, and primal-dual greedy strategies to efficiently handle both types of constraints. Extensive experiments on real-world trajectory and billboard datasets demonstrate that the proposed approach significantly outperforms baseline methods, achieving both high efficiency and strong effectiveness.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Billboard Advertising has emerged as an effective out-of-home advertising technique, where the goal is to select a limited number of slots and play advertisement content there, with the hope that it 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 $i \neq j$, $S_i \cap S_j=\emptyset$ 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. To solve the common-slot variant, we formulate the problem as a multi-submodular cover problem and design a bi-criteria approximation algorithm based on the continuous greedy framework and randomized rounding. For the disjoint-slot variant, we proposed a sampling-based approximation approach along with an efficient primal-dual greedy algorithm that enforces disjointness naturally. Extensive experiments with real-world trajectory and billboard datasets highlight the effectiveness and efficiency of the proposed solution approaches.

Problem

• Influence Maximization
• Billboard Advertising
• Multi-Product
• Trajectory-Driven
• Submodular Cover

Innovation

• multi-submodular cover
• influence maximization
• billboard advertising
• disjoint slot selection
• continuous greedy algorithm