Spillover Effect of Content Marketing in E-commerce Platform under the Fan Economy Era

As the proliferation of social media and live streaming, online celebrity endorsement is a common practice of content marketing in e-commerce platform. Despite the prevalent use of social media and online community, empirical research investigating the economic values of user-generated-content (UGC) and marketer-generated-content (MGC) still lags. This study seeks to contribute theoretically and practically to an understanding of how online celebrity endorsement and fans interaction behaviors affect e-commerce sales. We adopt cross-sectional regression to assess the economic value of online celebrity endorsement, and we employ panel vector autoregressive model to explain the dynamic relationship between marketers’ and consumers’ content marketing behaviors and e-commerce product sales. Empirical results highlight that the interaction within fans community has spillover effect on content marketing under “Fan Economy” era

Internet Celebrity Endorsement: How Internet Celebrities Bring Referral Traffic to E-commerce Sites?

Endorsement marketing has been widely used to generate consumer attention, interest, and purchase behaviors among targeted audience of celebrities. Internet celebrities who become famous by means of the Internet are more dependent on strategy intimacy to appeal to their followers. Limited studies have addressed the new business models in Internet celebrities economy: content advertising and online retailing. Our study aims to examine how Internet celebrity endorsement influencing the consumers’ clickon behaviors and purchase behaviors in the context of e-commerce business. Results suggest that content marketing using Internet celebrity endorsement exhibit a significant role in bringing referral traffic to e-commerce sites but less helpful to boost sales. The impact of Internet celebrity endorsement on consumers’ click-on decisions is U-shaped, but the role of Internet celebrities as online retailers will “shape-flip” such relationship to a negative linear relation. Therefore, Internet celebrity endorsement provides effective ways to bring referral traffic to e-commerce sites

Feature screening for clustering analysis

In this paper, we consider feature screening for ultrahigh dimensional clustering analyses. Based on the observation that the marginal distribution of any given feature is a mixture of its conditional distributions in different clusters, we propose to screen clustering features by independently evaluating the homogeneity of each feature's mixture distribution. Important cluster-relevant features have heterogeneous components in their mixture distributions and unimportant features have homogeneous components. The well-known EM-test statistic is used to evaluate the homogeneity. Under general parametric settings, we establish the tail probability bounds of the EM-test statistic for the homogeneous and heterogeneous features, and further show that the proposed screening procedure can achieve the sure independent screening and even the consistency in selection properties. Limiting distribution of the EM-test statistic is also obtained for general parametric distributions. The proposed method is computationally efficient, can accurately screen for important cluster-relevant features and help to significantly improve clustering, as demonstrated in our extensive simulation and real data analyses

Boosting the Discriminant Power of Naive Bayes

Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. The proposed stack auto-encoder consists of two auto-encoders for different purposes. The first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. The second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method with the regularized naive Bayes, the discrimination power of the model is greatly enhanced. The proposed method is evaluated on a set of machine-learning benchmark datasets. The experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.Comment: Accepted by 2022 International Conference on Pattern Recognitio

A Max-relevance-min-divergence Criterion for Data Discretization with Applications on Naive Bayes

In many classification models, data is discretized to better estimate its distribution. Existing discretization methods often target at maximizing the discriminant power of discretized data, while overlooking the fact that the primary target of data discretization in classification is to improve the generalization performance. As a result, the data tend to be over-split into many small bins since the data without discretization retain the maximal discriminant information. Thus, we propose a Max-Dependency-Min-Divergence (MDmD) criterion that maximizes both the discriminant information and generalization ability of the discretized data. More specifically, the Max-Dependency criterion maximizes the statistical dependency between the discretized data and the classification variable while the Min-Divergence criterion explicitly minimizes the JS-divergence between the training data and the validation data for a given discretization scheme. The proposed MDmD criterion is technically appealing, but it is difficult to reliably estimate the high-order joint distributions of attributes and the classification variable. We hence further propose a more practical solution, Max-Relevance-Min-Divergence (MRmD) discretization scheme, where each attribute is discretized separately, by simultaneously maximizing the discriminant information and the generalization ability of the discretized data. The proposed MRmD is compared with the state-of-the-art discretization algorithms under the naive Bayes classification framework on 45 machine-learning benchmark datasets. It significantly outperforms all the compared methods on most of the datasets.Comment: Under major revision of Pattern Recognitio