In online advertising, it is critical for advertisers to forecast conversion rate (CVR) of campaigns. Previous work on campaign forecasting concentrates on the time-series analysis which depend on the availability of a length of history. However, these approaches become inadequate for cold-start campaigns which lack for the observation of past. In this work, we attempt to mitigate this challenge by learning an unsupervised and composite campaign embedding to capture multi-view semantic relationships on campaign information, and consequently forecasting the cold-start campaigns using the nearest neighbor campaigns. Specifically, we propose a novel embedding framework which simultaneously extracts and fuses heterogeneous knowledge from multiple views of campaign data in a multi-task learning fashion, to learn the semantic relationship of ad message, conversion rule, and audience targeting. We develop a hierarchical attention mechanism to refine the embedding model at two levels - an intra-view attention to improve context aggregation, and an inter-task attention to balance task importance. Finally, we adopt the k-NN regression model to predict the CVR based on the neighboring campaigns in the embedding space which encodes the multi-view campaign proximity. We conduct extensive experiments on a real-world advertising campaign dataset. The results demonstrate the effectiveness of the proposed embedding method for CVR forecasting in cold-start scenarios.

Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing. In this paper, we propose a sparse tensor additive regression (STAR) that models a scalar response as a flexible nonparametric function of tensor covariates. The proposed model effectively exploits the sparse and low-rank structures in the tensor additive regression. We formulate the parameter estimation as a non-convex optimization problem, and propose an efficient penalized alternating minimization algorithm. We establish a non-asymptotic error bound for the estimator obtained from each iteration of the proposed algorithm, which reveals an interplay between the optimization error and the statistical rate of convergence. We demonstrate the efficacy of STAR through extensive comparative simulation studies, and an application to the click-through-rate prediction in online advertising.

Contextual multi-armed bandit algorithms have received significant attention in modeling users’ preferences for online personalized recommender systems in a timely manner. While significant progress has been made along this direction, a few major challenges have not been well addressed yet: (i) a vast majority of the literature is based on linear models that cannot capture complex non-linear inter-dependencies of user-item interactions; (ii) existing literature mainly ignores the latent relations among users and non-recommended items: hence may not properly reflect users’ preferences in the real-world; (iii) current solutions are mainly based on historical data and are prone to cold-start problems for new users who have no interaction history.

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations. In this work, we bridge this gap by developing a provable convex formulation of tensor co-clustering. Our convex co-clustering (CoCo) estimator enjoys stability guarantees and its computational and storage costs are polynomial in the size of the data. We further establish a non-asymptotic error bound for the CoCo estimator, which reveals a surprising “blessing of dimensionality” phenomenon that does not exist in vector or matrix-variate cluster analysis. Our theoretical findings are supported by extensive simulated studies. Finally, we apply the CoCo estimator to the cluster analysis of advertisement click tensor data from a major online company. Our clustering results provide meaningful business insights to improve advertising effectiveness.

We consider the estimation and inference of graphical models that characterize the dependency structure of high-dimensional tensor-valued data. To facilitate the estimation of the precision matrix corresponding to each way of the tensor, we assume the data follow a tensor normal distribution whose covariance has a Kronecker product structure. A critical challenge in the estimation and inference of this model is the fact that its penalized maximum likelihood estimation involves minimizing a non-convex objective function. To address it, this paper makes two contributions: (i) In spite of the non-convexity of this estimation problem, we prove that an alternating minimization algorithm, which iteratively estimates each sparse precision matrix while fixing the others, attains an estimator with an optimal statistical rate of convergence. (ii) We propose a de-biased statistical inference procedure for testing hypotheses on the true support of the sparse precision matrices, and employ it for testing a growing number of hypothesis with false discovery rate (FDR) control. The asymptotic normality of our test statistic and the consistency of FDR control procedure are established. Our theoretical results are backed up by thorough numerical studies and our real applications on neuroimaging studies of Autism spectrum disorder and users' advertising click analysis bring new scientific findings and business insights. The proposed methods are encoded into a publicly available R package Tlasso.

Click-through rate (CTR) is a critical problem in online advertising. Most existing researches only focus on the user-level CTR prediction. However, advertiser-level CTR forecasting also plays a very important role because advertisers typically decide how much they would like to bid for advertisements to achieve the maximum clicks given their budget based on CTR forecasting. Over-forecasting will make the advertiser to pay more than necessary but get less return on investment (ROI). Under-forecasting will make the advertiser to spend less money on campaigns but they cannot achieve the desired ROI goals. In this paper, we focus on the advertiser-level CTR forecasting and formulate it as a time series forecasting problem based on the historical CTR record. This is a very challenging problem due to the heavy fluctuation and highly non-linearity of time series. Furthermore, advertisers usually provide useful contextual information for their campaigns, such as text descriptions, targeting locations and devices, which has high correlation with CTR but has not yet been used for CTR forecasting. Thus, we propose a novel context-aware attention convolutional neural network (CACNN), which can capture the high non-linearity and local information of the time series, as well as the underlying correlation between the time series of CTR and the contextual information. To the best of our knowledge, this is the first work employing convolutional neural network and incorporating heterogeneous information to perform CTR forecasting at advertiser level. We implement the system on Yahoo TensorFlowOnSpark platform which enables distributed deep learning on a cluster of GPU and CPU servers, and achieves faster learning speed and data access on HDFS when available. The effectiveness of CACNN model has been demonstrated in real-world Yahoo advertising dataset, and therefore deployed in production with daily rolling of the model.

Advertising effectiveness measurement is a fundamental problem in online advertising. Various causal inference methods have been employed to measure the causal effects of ad treatments. However, existing methods mainly focus on linear logistic regression for univariate and binary treatments and are not well suited for complex ad treatments of multi-dimensions, where each dimension could be discrete or continuous. In this paper we propose a novel two-stage causal inference framework for assessing the impact of complex ad treatments. In the first stage, we estimate the propensity parameter via a sparse additive model; in the second stage, a propensity-adjusted regression model is applied for measuring the treatment effect. Our approach is shown to provide an unbiased estimation of the ad effectiveness under regularity conditions. To demonstrate the efficacy of our approach, we apply it to a real online advertising campaign to evaluate the impact of three ad treatments: ad frequency, ad channel, and ad size. We show that the ad frequency usually has a treatment effect cap when ads are showing on mobile device. In addition, the strategies for choosing best ad size are completely different for mobile ads and online ads.

As the online advertising industry has evolved into an age of diverse ad formats and delivery channels, users are exposed to complex ad treatments involving various ad characteristics. The diversity and generality of ad treatments call for accurate and causal measurement of ad effectiveness, i.e., how the ad treatment causes the changes in outcomes without the confounding effect by user characteristics. Various causal inference approaches have been proposed to measure the causal effect of ad treatments. However, most existing causal inference methods focus on univariate and binary treatment and are not well suited for complex ad treatments. Moreover, to be practical in the data-rich online environment, the measurement needs to be highly general and efficient, which is not addressed in conventional causal inference approaches. In this paper we propose a novel causal inference framework for assessing the impact of general advertising treatments. Our new framework enables analysis on uni- or multi-dimensional ad treatments, where each dimension (ad treatment factor) could be discrete or continuous. We prove that our approach is able to provide an unbiased estimation of the ad effectiveness by controlling the confounding effect of user characteristics. The framework is computationally efficient by employing a tree structure that specifies the relationship between user characteristics and the corresponding ad treatment. This tree-based framework is robust to model misspecification and highly flexible with minimal manual tuning. To demonstrate the efficacy of our approach, we apply it to two advertising campaigns. In the first campaign we evaluate the impact of different ad frequencies, and in the second one we consider the synthetic ad effectiveness across TV and online platforms. Our framework successfully provides the causal impact of ads with different frequencies in both campaigns. Moreover, it shows that the ad frequency usually has a treatment effect cap, which is usually over-estimated by naive estimation.

We propose a novel idea in the allocation and serving of online advertising. We show that by using predetermined fixed-length streams of ads (which we call patterns) to serve advertising, we can incorporate a variety of interesting features into the ad allocation optimization problem. In particular, our formulation optimizes for representativeness as well as user-level diversity and pacing of ads, under reach and frequency requirements. We show how the problem can be solved efficiently using a column generation scheme in which only a small set of best patterns are kept in the optimization problem. Our numerical tests suggest that with parallelization of the pattern generation process, the algorithm has a promising run time and memory usage.

In online advertising market it is crucial to provide advertisers with a reliable measurement of advertising effectiveness to make better marketing campaign planning. The basic idea for ad effectiveness measurement is to compare the performance (e.g., success rate) among users who were and who were not exposed to a certain treatment of ads. When a randomized experiment is not available, a naive comparison can be biased because exposed and unexposed populations typically have different features. One solid methodology for a fair comparison is to apply inverse propensity weighting with doubly robust estimation to the observational data. However the existing methods were not designed for the online advertising campaign, which usually suffers from huge volume of users, high dimensionality, high sparsity and imbalance. We propose an efficient framework to address these challenges in a real campaign circumstance. We utilize gradient boosting stumps for feature selection and gradient boosting trees for model fitting, and propose a subsampling-and-backscaling procedure that enables analysis on extremely sparse conversion data. The choice of features, models and feature selection scheme are validated with irrelevant conversion test. We further propose a parallel computing strategy, combined with the subsampling-and-backscaling procedure to reach computational efficiency. Our framework is applied to an online campaign involving millions of unique users, which shows substantially better model fitting and efficiency. Our framework can be further generalized to comparison of multiple treatments and more general treatment regimes, as sketched in the paper. Our framework is not limited to online advertising, but also applicable to other circumstances (e.g., social science) where a 'fair' comparison is needed with observational data.

Motivated by the problem of optimizing allocation in guaranteed display advertising, we develop an efficient, lightweight method of generating a compact allocation plan that can be used to guide ad server decisions. The plan itself uses just O(1) state per guaranteed contract, is robust to noise, and allows us to serve (provably) nearly optimally.

We consider the problem of pricing guaranteed contracts in online display advertising. This problem has two key characteristics that when taken together distinguish it from related offline and online pricing problems: (1) the guaranteed contracts are sold months in advance, and at various points in time, and (2) the inventory that is sold to guaranteed contracts - user visits - is very high-dimensional, having hundreds of possible attributes, and advertisers can potentially buy any of the very large number (many trillions) of combinations of these attributes. Consequently, traditional pricing methods such as real-time or combinatorial auctions, or optimization-based pricing based on self- and cross-elasticities are not directly applicable to this problem. We hence propose a new pricing method, whereby the price of a guaranteed contract is computed based on the prices of the individual user visits that the contract is expected to get. The price of each individual user visit is in turn computed using historical sales prices that are negotiated between a sales person and an advertiser, and we propose two different variants in this context. Our evaluation using real guaranteed contracts shows that the proposed pricing method is accurate in the sense that it can effectively predict the prices of other (out-of-sample) historical contracts.