Abstract
In spite of vast business potential, targeted advertising in public transportation systems is a grossly unexplored research area. For instance, SBS Transit in Singapore can reach 1 billion passengers per year but the annual advertising revenue contributes less than $35 million. To bridge the gap, we propose a probabilistic data model that captures the motion patterns and user interests so as to quantitatively evaluate the impact of an advertisement among the passengers. In particular, we leverage hundreds of millions of bus/train boarding transaction records to quantitatively estimate the probability as well as the extent of a user being influenced by an ad. Based on the influence model, we study a top- k retrieval problem for bus/train ad recommendation, which acts as a primitive operator to support various advanced applications. We solve the retrieval problem efficiently to support real-time decision making. In the experimental study, we use the dataset from SBS Transit as a case study to verify the effectiveness and efficiency of our proposed methodologies.
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