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.
conda env create --file environment.yaml
- pretrain : 0(Train), 1(Train with pretrained model), 2(Test)
- model_method : 0(G0E) 1(L2E) 2(G2E) 3(G5E)
- Change localpath to your path
python main.py --data_name 'foursquare2' --data_path '/localpath/LightMove/data/' --save_path /localpath/LightMove/train/ --pretrain 0 --loc_emb_size 100 --uid_emb_size 60 --tim_emb_size 10 --hidden_size 100 --dropout_p 0.3 --model_method 1 --epoch_max 50 --learning_rate 0.005
python main.py --data_name 'foursquare2' --data_path '/localpath/LightMove/data/' --save_path /localpath/LightMove/test/ --pretrain 2 --loc_emb_size 100 --uid_emb_size 60 --tim_emb_size 10 --hidden_size 100 --dropout_p 0.3 --model_method 1 --epoch_max 50 --learning_rate 0.005
python main.py --data_name 'foursquare2' --data_path '/localpath/LightMove/data/' --save_path /localpath/LightMove/test/ --pretrain 2 --loc_emb_size 100 --uid_emb_size 60 --tim_emb_size 10 --hidden_size 100 --dropout_p 0.3 --model_method 2 --epoch_max 50 --learning_rate 0.005
Jinsung Jeon, Minju Jo, Seunghyeon Cho, Noseong Park(Yonsei University)
Soyoung Kang(NAVER Clova)
Seonghoon Kim, Chiyoung Song(Motov Inc., Ltd.)