Brand key asset discovery via cluster-wise biased discriminant projection

Yang LIU, Zhonglei Gu, Tobey H. Ko, Jiming LIU

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Accurate and effective discovery of a brand's key assets, namely, Key Opinion Leaders (KOLs) and potential customers, plays an essential role in marketing campaigns. In a massive online social network, brands are challenged with identifying a small portion of key assets over an enormous volume of irrelevant users, making the problem a highly imbalanced one. Moreover, having to deal with social media data that are usually high-dimensional, the task of brand key asset discovery can be immensely expensive yet inaccurate if the information are not processed efficiently to extract representative features from the original space prior to the learning process. To address the above issues, we propose a novel method dubbed Cluster-wise Biased Discriminant Projection (CBDP) to uncover the compact and informative features from users' data for brand key asset discovery. CBDP conducts a two-layer learning procedure. In the first layer, a Discriminant Clustering (DC) scheme is developed to partition the original dataset into clusters with maximum discriminant capacity. In the second layer, a Biased Discriminant Projection (BDP) algorithm is proposed and performed on each cluster to map the high-dimensional data to the low-dimensional subspace, where the discriminant information of classes with high importance/preference is preserved. A unified mapping function of CBDP is finally established by integrating these two layers. Experiments on both synthetic examples and a real-world brand key asset dataset validate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
PublisherAssociation for Computing Machinery, Inc
Pages284-290
Number of pages7
ISBN (Electronic)9781450349512
DOIs
Publication statusPublished - 23 Aug 2017
Event16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 - Leipzig, Germany
Duration: 23 Aug 201726 Aug 2017

Publication series

NameProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017

Conference

Conference16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
Country/TerritoryGermany
CityLeipzig
Period23/08/1726/08/17

Scopus Subject Areas

  • Computer Networks and Communications
  • Artificial Intelligence
  • Software

User-Defined Keywords

  • Brand key asset discovery
  • Clusterwise biased discriminant projection
  • Supervised feature extraction

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