{"type":"ScholarlyArticle","title":"Spatio-Temporal Check-in Time Prediction with Recurrent Neural Network based Survival Analysis","authors":[{"name":"Guolei Yang","slug":"guolei-yang","given":"Guolei","family":"Yang","affiliation":""},{"name":"Ying Cai","slug":"ying-cai","given":"Ying","family":"Cai","affiliation":"Department of Computer Science , Iowa State University"},{"name":"Chandan K Reddy","slug":"chandan-k-reddy","given":"Chandan K.","family":"Reddy","affiliation":"Department of Computer Science , Virginia Tech."}],"abstract":"We introduce a novel check-in time prediction problem. The goal is to predict the time a user will check-in to a given location. We formulate check-in prediction as a survival analysis problem and propose a Recurrent-Censored Regression (RCR) model. We address the key challenge of check-in data scarcity, which is due to the uneven distribution of check-ins among users/locations. Our idea is to enrich the check-in data with potential visitors, i.e., users who have not visited the location before but are likely to do so. RCR uses recurrent neural network to learn latent representations from historical check-ins of both actual and potential visitors, which is then incorporated with censored regression to make predictions. Experiments show RCR outperforms state-of-the-art event time prediction techniques on real-world datasets.","doi":"10.24963/ijcai.2018/413","url":"https://ijcai.org/proceedings/2018/413","pdf":"https://ijcai.org/proceedings/2018/0413.pdf","markdown":"https://ijcai.org/proceedings/2018/413.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/413","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":413,"pages":{"first":2976,"last":2983},"keywords":["Machine Learning: Deep Learning","Machine Learning Applications: Applications of Supervised Learning","Machine Learning: New Problems"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 1993] Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. 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