{"type":"ScholarlyArticle","title":"HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction","authors":[{"name":"Dejiang Kong","slug":"dejiang-kong","given":"Dejiang","family":"Kong","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Fei Wu","slug":"fei-wu","given":"Fei","family":"Wu","affiliation":"College of Computer Science and Technology , Zhejiang University"}],"abstract":"The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.","doi":"10.24963/ijcai.2018/324","url":"https://ijcai.org/proceedings/2018/324","pdf":"https://ijcai.org/proceedings/2018/0324.pdf","markdown":"https://ijcai.org/proceedings/2018/324.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/324","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":324,"pages":{"first":2341,"last":2347},"keywords":["Machine Learning: Learning Preferences or Rankings","Machine Learning: Neural Networks","Machine Learning: Machine Learning","Machine Learning: Deep Learning","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Begleiter et al., 2004] Ron Begleiter, Ran El-Yaniv, and Golan Yona. 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