Jul 17, 2019 · ST-HIN predicts the next POI of users from their spatial–temporal incomplete historical check-in sequences, and uses the multi-modal recurrent neural network ...
A novel attentional meta-path-based recurrent neural network is proposed, dubbed ST-HIN, which predicts the next POI of users from their spatial–temporal ...
ST-HIN predicts the next POI of users from their spatial–temporal incomplete historical check-in sequences, and uses the multi-modal recurrent neural network to ...
For example, based on user historical check-in data, we can analyze and predict where a user will go next. Moreover, such analysis can also be used for social.
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In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neigh- bors based on heterogeneous factors. A ...
It uniquely considers spatial and temporal factors in shaping check-in behaviors, offering a comprehensive global view of location transitions. Crucially, ...
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Sep 27, 2021 · Predicting the next location: A recurrent model with spatial and temporal contexts. ... A meta-learning approach for spatial-temporal prediction.
Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation. An Attentional Recurrent Neural Network for ...
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Predicting the next location: A recurrent model with spatial and temporal contexts. In AAAI, 2016. [Lu et al.,2020] Yuanfu Lu, Yuan Fang, and Chuan Shi ...
Aug 18, 2023 · Liu, Q., et al.: Predicting the next location: A recurrent model with spatial and temporal contexts. In: AAAI. pp. 194–200 (2016). 12. Qian ...
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