@inproceedings{0f24731b72034cae9301c54dea3e1fe0,
title = "Time-Aware Location Prediction by Convolutional Area-of-Interest Modeling and Memory-Augmented Attentive LSTM (Extended abstract)",
abstract = "Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called {"}t-LocPred{"}. It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods ({"}ConvAoI{"}); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model ({"}mem-attLSTM{"}) to capture long-term behavior patterns. Experimental results show that t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.",
keywords = "area-of-interests modeling, attention, location prediction, memory augmentation, time-aware",
author = "Liu, {Chi Harold} and Yu Wang and Chengzhe Piao and Zipeng Dai and Ye Yuan and Guoren Wang and Dapeng Wu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 39th IEEE International Conference on Data Engineering, ICDE 2023 ; Conference date: 03-04-2023 Through 07-04-2023",
year = "2023",
doi = "10.1109/ICDE55515.2023.00357",
language = "English",
series = "Proceedings - International Conference on Data Engineering",
publisher = "IEEE Computer Society",
pages = "3861--3862",
booktitle = "Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023",
address = "United States",
}