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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.authorZhang, Xiangliang
dc.date.accessioned2021-02-24T08:58:37Z
dc.date.available2021-02-24T08:58:37Z
dc.date.issued2021
dc.identifier.citationHan, P., Shang, S., Sun, A., Zhao, P., Zheng, K., & Zhang, X. (2021). Point-of-Interest Recommendation with Global and Local Context. IEEE Transactions on Knowledge and Data Engineering, 1–1. doi:10.1109/tkde.2021.3059744
dc.identifier.issn2326-3865
dc.identifier.doi10.1109/TKDE.2021.3059744
dc.identifier.urihttp://hdl.handle.net/10754/667654
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. Many studies have shown that geographic information plays an important role in POI recommendation. In this study, we focus on two levels geographic information: local similarity and global similarity. We further show that AUC-MF can be easily extended to incorporate geographical contextual information for POI recommendation.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9355002/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9355002
dc.rights(c) 2021 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 Recommendation
dc.subjectAUC
dc.subjectMatrix Factorization
dc.subjectContext
dc.titlePoint-of-Interest Recommendation with Global and Local Context
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentECRC, King Abdullah University of Science and Technology, 127355 Thuwal, Jeddah, Saudi Arabia, 23955-6900
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineering
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Engineering, Nanyang Technological University, Singapore, Singapore, Singapore, 639798
dc.contributor.institutionAI Lab, Tencent, 508929 Shenzhen, Guangdong, China,
dc.contributor.institutionComputer Science, University of Electronic Science and Technology of China, 12599 Chengdu, Sichuan, China,
dc.identifier.pages1-1
kaust.personHan, Peng
kaust.personShang, Shuo
kaust.personZhang, Xiangliang


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