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dc.contributor.authorYin, Hongzhi
dc.contributor.authorHu, Zhiting
dc.contributor.authorZhou, Xiaofang
dc.contributor.authorWang, Hao
dc.contributor.authorZheng, Kai
dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorSadiq, Shazia
dc.date.accessioned2018-04-17T01:30:34Z
dc.date.available2018-04-17T01:30:34Z
dc.date.issued2016
dc.identifier.doi10.1109/ICDE.2016.7498303
dc.identifier.urihttp://hdl.handle.net/10072/348007
dc.description.abstractSocial community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic (UCGT), to simulate the generative process of communities as a result of network proximities, spatiotemporal co-occurrences and semantic similarity. With a well-designed multi-component model structure and a parallel inference implementation to leverage the power of multicores and clusters, our UCGT model is expressive while remaining efficient and scalable to growing large-scale geo-social networking data. We deploy UCGT to two application scenarios of user behavior predictions: check-in prediction and social interaction prediction. Extensive experiments on two large-scale geo-social networking datasets show that UCGT achieves better performance than existing state-of-the-art comparison methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameICDE 2016
dc.relation.ispartofconferencetitleProceedings of the 2016 IEEE 32nd International Conference on Data Engineering
dc.relation.ispartofdatefrom2016-05-16
dc.relation.ispartofdateto2016-05-20
dc.relation.ispartoflocationHelsinki, Finland
dc.subject.fieldofresearchDatabase Management
dc.subject.fieldofresearchcode080604
dc.titleDiscovering Interpretable Geo-Social Communities for User Behavior Prediction
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


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