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首页> 外文期刊>IEEE transactions on industrial informatics >Efficiently Targeted Billboard Advertising Using Crowdsensing Vehicle Trajectory Data
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Efficiently Targeted Billboard Advertising Using Crowdsensing Vehicle Trajectory Data

机译：有效地使用众群车辆轨迹数据定位广告牌广告

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摘要

Different from online promotion, the outdoor billboard advertising industry suffers from a lack of audience-targeted delivery and quantitative dissemination evaluation, which undermine its impact in practice and hinder it from fast development. To bridge this gap, in this paper, we leverage crowdsensing vehicle trajectory data to empower audience-targeted billboard advertising. More specifically, by integrating the information of mobility transition, traffic conditions (traffic volume and average speed), and advertisement semantic topics, we propose a quantitative model to quantify advertisement influence spread, with a special consideration on influence overlapping among mobile users. Based on it, an influence maximization-targeted billboard advertising problem is formulated to find k advertising units over spatiotemporal dimensions, with the goal of maximizing the total expected advertisement influence spread. To tackle the efficiency issue for solving large combinatorial optimization problem, we employ a divide-and-conquer mechanism, and propose a utility evaluation-based optimal searching approach. Extensive experiments on real-world taxicab trajectories clearly validate the effectiveness and efficiency of our proposed approach.
机译：与在线促销不同，户外广告牌广告行业缺乏受众潜入的递送和量化传播评估，这破坏了其在实践中的影响并阻碍了快速发展。为了弥合这个差距，在本文中，我们利用众群的车辆轨迹数据来赋予受众瞄准的广告牌广告。更具体地，通过整合移动性转型，交通状况（交通量和平均速度）和广告语义主题的信息，我们提出了定量模型来量化广告影响扩散，特别考虑到移动用户之间的影响重叠。基于它，制定了影响最大化目标广告牌广告问题，以查找k广告单位在时尚尺寸上，其目标最大化预期的广告影响。为了解决求解大型组合优化问题的效率问题，我们采用了划分和征服机制，并提出了一种基于实用评估的最优搜索方法。对现实世界出租车轨迹的广泛实验明确验证了我们提出的方法的有效性和效率。

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