Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Abstract

The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers' implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three realworld datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies.

Scoped literature review of artificial intelligence marketing adoptions for ad optimization with reinforcement learning

“…To maximize the profit for the advertiser, Lou et al 7 decide to use dynamic billboards where an agent decides what to display. Changing ads dynamically according to the situation, is the problem called the dynamic ad problem 7 .…”

Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving

“…The world is intensively focusing on the emerging technology of autonomous driving to counter transportation issues in urban areas like road accidents, traffic congestion, parking space, and redundancy issues [152], [178]. AV began in the early 1980s, primarily in the United States and Europe, leading to increasing advances in driving competence in a variety of situations [166], [174], [175], [176], [177].…”

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

“…Wang et al [200] also consider the constraint of budget, and they use a divide-and-conquer strategy to improve the efficiency of placing billboards on road networks. Taking into account many factors (e.g., the customers' interest, the cooperation and competition among billboards) influencing the benefit of billboards, Lou et al [201] formulate the dynamic advertising problem to maximize the commercial profit. More specifically, they first use the vehicular data (e.g., trajectories and preferences) to extract potential customers' implicit information.…”