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dc.contributor.authorYin, Hongzhien_US
dc.contributor.authorChen, Liangen_US
dc.contributor.authorWang, Weiqingen_US
dc.contributor.authorDu, Xingzhongen_US
dc.contributor.authorNguyen, Quoc Viet Hungen_US
dc.contributor.authorZhou, Xiaofangen_US
dc.date.accessioned2018-08-14T12:30:25Z
dc.date.available2018-08-14T12:30:25Z
dc.date.issued2017en_US
dc.identifier.doi10.1109/ICDE.2017.43en_US
dc.identifier.urihttp://hdl.handle.net/10072/348003
dc.description.abstractWith the rapid prevalence of smart mobile devices and the dramatic proliferation of mobile applications (Apps), App recommendation becomes an emergent task that will benefit different stockholders of mobile App ecosystems. Unlike traditional items, Apps have privileges to access a user's sensitive resources (e.g., contacts, messages and locations) which may lead to security risk or privacy leak. Thus, users' choosing of Apps are influenced by not only their personal interests but also their privacy preferences. Moreover, user privacy preferences vary with App categories. In this paper, we propose a mobile sparse additive generative model (Mobi-SAGE) to recommend Apps by considering both user interests and category-aware user privacy preferences. We collected a real-world dataset from 360 App store - the biggest Android App platform in China, and conduct extensive experiments on it. The experimental results show that our Mobi-SAGE consistently and significantly outperforms the state-of-the-art approaches, which implies the importance of exploiting category-aware user privacy preferences.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofconferencenameICDE 2017en_US
dc.relation.ispartofconferencetitleProceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE 2017)en_US
dc.relation.ispartofdatefrom2017-04-19en_US
dc.relation.ispartofdateto2017-04-22en_US
dc.relation.ispartoflocationSan Diego, California, USAen_US
dc.subject.fieldofresearchDatabase Managementen_US
dc.subject.fieldofresearchcode080604en_US
dc.titleMobi-SAGE: A Sparse Additive Generative Model for Mobile App Recommendationen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
dc.description.versionPost-printen_US
gro.rights.copyright© 2017 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.en_US
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


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