Show simple item record

dc.contributor.authorYin, Hongzhi
dc.contributor.authorChen, Liang
dc.contributor.authorWang, Weiqing
dc.contributor.authorDu, Xingzhong
dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2018-08-14T12:30:25Z
dc.date.available2018-08-14T12:30:25Z
dc.date.issued2017
dc.identifier.doi10.1109/ICDE.2017.43
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameICDE 2017
dc.relation.ispartofconferencetitleProceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE 2017)
dc.relation.ispartofdatefrom2017-04-19
dc.relation.ispartofdateto2017-04-22
dc.relation.ispartoflocationSan Diego, California, USA
dc.subject.fieldofresearchDatabase systems
dc.subject.fieldofresearchcode460505
dc.titleMobi-SAGE: A Sparse Additive Generative Model for Mobile App Recommendation
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
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.
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record