Display advertising has been a significant source of revenue for publishers and ad networks in online advertising ecosystem. One important business model in online display advertising is Ad Exchange marketplace, also called nonguaranteed delivery (NGD), in which advertisers buy targeted page views and audiences on a spot market through real-time auction. In this paper, we describe a bid landscape forecasting system in NGD marketplace for any advertiser campaign specified by a variety of targeting attributes. In the system, the impressions that satisfy the campaign targeting attributes are partitioned into multiple mutually exclusive samples. Each sample is one unique combination of quantified attribute values. We develop a divide-andconquer approach that breaks down the campaign-level forecasting problem. First, utilizing a novel star-tree data structure, we forecast the bid for each sample using non-linear regression by gradient boosting decision trees. Then we employ a mixture-of-log-normal model to generate campaignlevel bid distribution based on the sample-level forecasted distributions. The experiment results of a system developed with our approach show that it can accurately forecast the bid distributions for various campaigns running on the world's largest NGD advertising exchange system, outperforming two baseline methods in term of forecasting errors.
Oversampling is a promising preprocessing technique for imbalanced datasets which generates new minority instances to balance the dataset. However, improper generated minority instances, i.e., noise instances, may interfere the learning of the classifier and impact it negatively. Given this, in this paper, we propose a simple and effective oversampling approach known as ASN-SMOTE based on the k-nearest neighbors and the synthetic minority oversampling technology (SMOTE). ASN-SMOTE first filters noise in the minority class by determining whether the nearest neighbor of each minority instance belongs to the minority or majority class. After that, ASN-SMOTE uses the nearest majority instance of each minority instance to effectively perceive the decision boundary, inside which the qualified minority instances are selected adaptively for each minority instance by the proposed adaptive neighbor selection scheme to synthesize new minority instance. To substantiate the effectiveness, ASN-SMOTE has been applied to three different classifiers and comprehensive experiments have been conducted on 24 imbalanced benchmark datasets. ASN-SMOTE is also extensively compared with nine notable oversampling algorithms. The results show that ASN-SMOTE achieves the best results in the majority of datasets. The ASN-SMOTE implementation is available at: https://www.github.com/yixinkai123/ASN-SMOTE/.
We consider an inverse transport problem in fluorescence ultrasound modulated optical tomography (fUMOT) with angularly averaged illuminations and measurements. We study the uniqueness and stability of the reconstruction of the absorption coefficient and the quantum efficiency of the fluorescent probes. Reconstruction algorithms are proposed and numerical validations are performed. This paper is an extension of [29], where a diffusion model for this problem was considered.
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