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• W3065432481 abstract "Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance." @default.
• W3065432481 created "2020-08-24" @default.
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• W3065432481 date "2020-12-01" @default.
• W3065432481 modified "2023-10-16" @default.
• W3065432481 title "Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification" @default.
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• W3065432481 doi "https://doi.org/10.1016/j.neunet.2020.08.015" @default.
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