{"type":"ScholarlyArticle","title":"LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks","authors":[{"name":"Ankita Likhyani","slug":"ankita-likhyani","given":"Ankita","family":"Likhyani","affiliation":"Indraprastha Institute of Information Technology , Delhi , India"},{"name":"Srikanta Bedathur","slug":"srikanta-bedathur","given":"Srikanta","family":"Bedathur","affiliation":"IBM-Research Lab , Delhi , India; Queen's University Belfast Northern Ireland , UK"},{"name":"Deepak P","slug":"deepak-p","given":"Deepak","family":"P","affiliation":""}],"abstract":"Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we focus on the problem of location promotion and develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a joint model called LoCaTe, consisting of (i) user mobility model estimated using kernel density estimates; (ii) a model of the semantics of the location using topic models; and (iii) a model of time-gap between check-ins using exponential distribution. We validate our model on a long-term crawl of Foursquare data collected between Jan 2015 Feb 2016, as well as on publicly available LBSN datasets. Our experiments demonstrate that LoCaTe significantly outperforms state-of-the-art models for the same task.","doi":"10.24963/ijcai.2017/314","url":"https://ijcai.org/proceedings/2017/314","pdf":"https://ijcai.org/proceedings/2017/0314.pdf","markdown":"https://ijcai.org/proceedings/2017/314.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/314","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":314,"pages":{"first":2259,"last":2265},"keywords":["Machine Learning: New Problems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Blei et al., 2003] David M. 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