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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.accessioned2019-07-08T12:27:11Z
dc.date.available2019-07-08T12:27:11Z
dc.date.issued2019-04
dc.identifier.citationHan, P., Shang, S., Sun, A., Zhao, P., Zheng, K., & Kalnis, P. (2019). AUC-MF: Point of Interest Recommendation with AUC Maximization. 2019 IEEE 35th International Conference on Data Engineering (ICDE). doi:10.1109/icde.2019.00141
dc.identifier.doi10.1109/ICDE.2019.00141
dc.identifier.urihttp://hdl.handle.net/10754/655954
dc.description.abstractThe task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8731461/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8731461
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectPOI recommendaton
dc.subjectmatrix factorization
dc.subjectAUC
dc.titleAUC-MF: Point of Interest Recommendation with AUC Maximization
dc.typeConference Paper
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.date8-11 April 2019
dc.conference.name2019 IEEE 35th International Conference on Data Engineering (ICDE)
dc.conference.locationMacao, Macao
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science and Engineering, Nanyang Technological University, Singapore
dc.contributor.institutionInception Institute of Artificial Intelligence
dc.contributor.institutionTencent AI Lab, China
dc.contributor.institutionBig Data Research Center, University of Electronic Science and Technology of China, China
kaust.personHan, Peng
kaust.personKalnis, Panos
refterms.dateFOA2019-07-10T06:49:32Z


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