@article{
 title = {Clustering social audiences in business information networks},
 type = {article},
 year = {2020},
 keywords = {Business information networks,Clustering,Machine learning,Social networks},
 pages = {107126},
 volume = {100},
 id = {2de224fd-7fc4-3bb8-b210-06b4a0e65cbe},
 created = {2019-12-13T23:59:00.000Z},
 file_attached = {false},
 profile_id = {079852a8-52df-3ac8-a41c-8bebd97d6b2b},
 last_modified = {2022-04-10T12:10:46.577Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {032bc9cb-8256-40c2-a1d8-d016d563e89a,2327f56c-ffc0-4246-bac0-b9fa6098ebfb},
 private_publication = {false},
 abstract = {Business information networks involve diverse users and rich content and have emerged as important platforms for enabling business intelligence and business decision making. A key step in an organizations business intelligence process is to cluster users with similar interests into social audiences and discover the roles they play within a business network. In this article, we propose a novel machine-learning approach, called CBIN, that co-clusters business information networks to discover and understand these audiences. The CBIN framework is based on co-factorization. The audience clusters are discovered from a combination of network structures and rich contextual information, such as node interactions and node-content correlations. Since what defines an audience cluster is data-driven, plus they often overlap, pre-determining the number of clusters is usually very difficult. Therefore, we have based CBIN on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data. Experiments validate an outstanding performance by CBIN compared to other state-of-the-art algorithms on 13 real-world enterprise datasets.},
 bibtype = {article},
 author = {Zheng, Yu and Hu, Ruiqi and Fung, Sai fu and Yu, Celina and Long, Guodong and Guo, Ting and Pan, Shirui},
 doi = {10.1016/j.patcog.2019.107126},
 journal = {Pattern Recognition}
}

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