TY - GEN
T1 - Brand key asset discovery via cluster-wise biased discriminant projection
AU - Liu, Yang
AU - Gu, Zhonglei
AU - Ko, Tobey H.
AU - Liu, Jiming
N1 - Funding Information:
e authors would like to thank the reviewers for their constructive comments and suggestions. is work was supported in part by the National Natural Science Foundation of China under Grant 61503317, in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032, and in part by the Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation (No. JTKF2017001), Changsha University of Science & Technology, P.R. China.
PY - 2017/8/23
Y1 - 2017/8/23
N2 - 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.
AB - 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.
KW - Brand key asset discovery
KW - Clusterwise biased discriminant projection
KW - Supervised feature extraction
UR - http://www.scopus.com/inward/record.url?scp=85031033630&partnerID=8YFLogxK
U2 - 10.1145/3106426.3106516
DO - 10.1145/3106426.3106516
M3 - Conference proceeding
AN - SCOPUS:85031033630
T3 - Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
SP - 284
EP - 290
BT - Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
PB - Association for Computing Machinery (ACM)
T2 - 16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
Y2 - 23 August 2017 through 26 August 2017
ER -