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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Spatiotemporal-Aware Region Recommendation with Deep Metric Learning
verfasst von : Hengpeng Xu, Yao Zhang, Jinmao Wei, Zhenglu Yang, Jun Wang
Erschienen in: Database Systems for Advanced Applications
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Abstract
Personalized points of interests (POI) recommendation is an important basis for location-based services. A typical application scenario is to recommend a region with reliable POIs to a user when he/she travels to an unfamiliar area without any background knowledge. In this study, we explore spatiotemporal-aware region recommendation to manage this learning task. We propose a unified deep learning model that comprehensively incorporates dynamic personal and global user preferences across regions, along with spatiotemporal dependencies, into check-in region history. We model and fuse user preferences through a pyramidal ConvLSTM component, and capture the dynamic region attributes through a recurrent component. Two components are seamlessly assembled in a unified framework to yield next time region recommendation. Extensive experiments on real-word datasets demonstrate the effectiveness of the proposed model.