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dc.contributor.authorHan, Peng
dc.contributor.authorShang, Shuo
dc.contributor.authorSun, Aixin
dc.contributor.authorZhao, Peilin
dc.contributor.authorZheng, Kai
dc.contributor.authorKalnis, Panos
dc.date.accessioned2019-06-26T11:38:21Z
dc.date.available2019-06-26T11:38:21Z
dc.date.issued2019-01-13
dc.identifier.urihttp://hdl.handle.net/10754/655724
dc.description.abstractAUC-MF: Point of Interest Recommendation with AUC Maximization Location-based social networks (LSBNs) allow users to check in and share their experiences when they visit a point of interest (POI), such as a museum or a restaurant. With the development and popularity of various LSBN (Fig. 1) platforms e.g., BrightKite, Foursquare, and Gowalla, user check-in data is growing at an unprecedented pace. For instance, Foursquare had more than 50 million active users and more than 8 billion check-ins made by 2016. The availability of abundant amount of user check-in data, enables many studies on recommender systems to further enhance user experiences. POI recommendation aims at finding unvisited locations that a user may be interested in, by learning from users check-in history and other related factors. POI recommendation is challenging for many reasons. One of the most important reasons is that user check-in data is extremely sparse .
dc.relation.urlhttps://epostersonline.com/wep2019/node/91
dc.titleAUC-MF: Point of Interest Recommendation with AUC Maximization
dc.typePoster
dc.conference.dateJANUARY 13 - 17 , 2019
dc.conference.nameWEP Library ePoster competition 2019
dc.conference.locationKAUST
refterms.dateFOA2019-06-26T11:38:21Z


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