Brand key asset discovery via cluster-wise biased discriminant projection

Yang Liu, Zhonglei Gu, Tobey H. Ko, Jiming Liu
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引用次数: 3

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.
品牌关键资产发现通过集群明智的偏见判别投影
准确有效地发现品牌的关键资产,即关键意见领袖(kol)和潜在客户,在营销活动中起着至关重要的作用。在一个庞大的在线社交网络中,品牌面临着在大量无关用户中识别一小部分关键资产的挑战,这使得问题变得高度不平衡。此外,由于必须处理通常是高维的社交媒体数据,如果在学习过程之前没有有效地处理信息以从原始空间中提取代表性特征,那么品牌关键资产发现的任务可能会非常昂贵且不准确。为了解决上述问题,我们提出了一种新的方法,称为聚类有偏差判别投影(CBDP),从用户数据中揭示紧凑和信息丰富的特征,用于品牌关键资产发现。CBDP是一个两层学习过程。在第一层,提出了一种判别聚类(DC)方案,将原始数据集划分为具有最大判别能力的聚类。在第二层,提出了一种偏差判别投影(Biased Discriminant Projection, BDP)算法,并在每个聚类上执行该算法,将高维数据映射到低维子空间,在低维子空间中保留高重要性/偏好类的判别信息。将这两层进行整合,最终建立了统一的CBDP映射函数。在合成示例和真实品牌密钥资产数据集上的实验验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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