LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising
This work addresses the need for efficient next-POI recommendation in the domain of taxicab rooftop advertising, offering incremental improvements in model performance for targeted marketing applications.
The paper tackles the problem of predicting taxicabs' next locations for targeted advertising by proposing LightMove, a lightweight deep learning method based on neural ordinary differential equations (NODEs) that achieves higher prediction accuracy, fewer parameters, and faster training/inference times compared to state-of-the-art models.
Mobile digital billboards are an effective way to augment brand-awareness. Among various such mobile billboards, taxicab rooftop devices are emerging in the market as a brand new media. Motov is a leading company in South Korea in the taxicab rooftop advertising market. In this work, we present a lightweight yet accurate deep learning-based method to predict taxicabs' next locations to better prepare for targeted advertising based on demographic information of locations. Considering the fact that next POI recommendation datasets are frequently sparse, we design our presented model based on neural ordinary differential equations (NODEs), which are known to be robust to sparse/incorrect input, with several enhancements. Our model, which we call LightMove, has a larger prediction accuracy, a smaller number of parameters, and/or a smaller training/inference time, when evaluating with various datasets, in comparison with state-of-the-art models.