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Dynamic Recommendation of POI Sequence Responding to Historical Trajectory

Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China
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ISPRS Int. J. Geo-Inf. 2019, 8(10), 433; https://doi.org/10.3390/ijgi8100433
Received: 9 August 2019 / Revised: 23 September 2019 / Accepted: 27 September 2019 / Published: 30 September 2019
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq. View Full-Text
Keywords: POI sequence recommendation; location-based social networks; deep neural network; sequence-to-sequence POI sequence recommendation; location-based social networks; deep neural network; sequence-to-sequence
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Huang, J.; Liu, Y.; Chen, Y.; Jia, C. Dynamic Recommendation of POI Sequence Responding to Historical Trajectory. ISPRS Int. J. Geo-Inf. 2019, 8, 433.

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