mlsanityData to 7 Oct 2026

LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising

arXiv 2108.04993 ↗First posted 11 Aug 2021cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 11 Aug 2021.

Builds on

18 of 43 references.

References · 18

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  4. 2019Neural Graph Collaborative Filteringcited by 660
  5. 2019Augmented Neural ODEscited by 396
  6. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 230
  7. 2016FractalNet: Ultra-Deep Neural Networks without Residualscited by 216
  8. 2017Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equationscited by 203
  9. 2020Dissecting Neural ODEscited by 150
  10. 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 119
  11. 2019On Robustness of Neural Ordinary Differential Equationscited by 83
  12. 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
6 more
  1. 2021LT-OCF: Learnable-Time ODE-based Collaborative Filteringcited by 21
  2. 2018Convolutional Neural Networks combined with Runge-Kutta Methodscited by 18
  3. 2019Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glandscited by 15
  4. 2021ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equationscited by 15
  5. 2016We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!cited by 9
  6. 2017NEXT: A Neural Network Framework for Next POI Recommendationcited by 7

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 3.

9 papers.

  1. 2024Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Modelscited by 21
  2. 2025GeoGen: A Two-stage Coarse-to-Fine Framework for Fine-grained Synthetic Location-based Social Network Trajectory Generationcited by 12
  3. 2022Predicting Human Mobility via Self-supervised Disentanglement Learningcited by 5
  4. 2024Spatial-Temporal Cross-View Contrastive Pre-training for Check-in Sequence Representation Learningcited by 2
  5. 2023Hawkes Process Based on Controlled Differential Equationscited by 1
  6. 2023Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equationscited by 1
  7. 2024Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglementcited by 0
  8. 2025Can We Predict Your Next Move Without Breaking Your Privacy?cited by 0
  9. 2025GSTM-HMU: Generative Spatio-Temporal Modeling for Human Mobility Understandingcited by 0