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Dynamic connection-based social group recommendation
journal contribution
posted on 2020-03-01, 00:00 authored by D Qin, X Zhou, L Chen, Guangyan HuangGuangyan Huang, Y ZhangGroup recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach.
History
Journal
IEEE transactions on knowledge and data engineeringVolume
32Issue
3Pagination
453 - 467Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
ISSN
1041-4347eISSN
1558-2191Language
EngPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2018 IEEEUsage metrics
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No categories selectedKeywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Information SystemsEngineering, Electrical & ElectronicComputer ScienceEngineeringRecommender systemsCollaborationSocial groupsMotion picturesAustraliaData miningMediaGroup recommendationsocial itemsocial connectioncollaborative filteringSIMILARITYSVD
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