LightMove：出租车车顶广告的轻量级Next-POI推荐
LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising
摘要：
移动数字广告牌是一种有效的方式来增强品牌知名度.在各种这样的移动广告牌中，出租车屋顶设备作为一种全新的媒体在市场上出现。Motov是韩国出租车车顶广告市场的领先公司。在这项工作中，我们提出了一种轻量级但准确的基于深度学习的方法来预测出租车的下一个位置，以更好地准备基于位置的人口统计信息的定向广告。考虑到下一个POI推荐数据集经常是稀疏的，我们设计了我们提出的模型的基础上神经常微分方程（NODE），这是已知的稀疏/不正确的输入是鲁棒的，有几个增强。我们的模型，我们称之为LightMove，与最先进的模型相比，在使用各种数据集进行评估时，具有更高的预测精度，更少的参数数量和/或更短的训练/推理时间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
