Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: KDD 2019. The reproducible code and dataset link is https://github.com/rk2900/DL

Deep Recurrent Survival Analysis

Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at fine-grained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three real-world tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code: https://github.com/rk2900/drs

Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model

Over the past decade, programmatic advertising has received a great deal of attention in the online advertising industry. A real-time bidding (RTB) system is rapidly becoming the most popular method to buy and sell online advertising impressions. Within the RTB system, demand-side platforms (DSP) aim to spend advertisers' campaign budgets efficiently while maximizing profit, seeking impressions that result in high user responses, such as clicks or installs. In the current study, we investigate the process of predicting a mobile gaming app installation from the point of view of a particular DSP, while paying attention to user privacy, and exploring the trade-off between privacy preservation and model performance. There are multiple levels of potential threats to user privacy, depending on the privacy leaks associated with the data-sharing process, such as data transformation or de-anonymization. To address these concerns, privacy-preserving techniques were proposed, such as cryptographic approaches, for training privacy-aware machine-learning models. However, the ability to train a mobile gaming app installation prediction model without using user-level data, can prevent these threats and protect the users' privacy, even though the model's ability to predict may be impaired. Additionally, current laws might force companies to declare that they are collecting data, and might even give the user the option to opt out of such data collection, which might threaten companies' business models in digital advertising, which are dependent on the collection and use of user-level data. We conclude that privacy-aware models might still preserve significant capabilities, enabling companies to make better decisions, dependent on the privacy-efficacy trade-off utility function of each case.Comment: 11 pages, 3 figure

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

Learning and predicting user responses, such as clicks and conversions, are crucial for many Internet-based businesses including web search, e-commerce, and online advertising. Typically, a user response model is established by optimizing the prediction accuracy, e.g., minimizing the error between the prediction and the ground truth user response. However, in many practical cases, predicting user responses is only part of a rather larger predictive or optimization task, where on one hand, the accuracy of a user response prediction determines the final (expected) utility to be optimized, but on the other hand, its learning may also be influenced from the follow-up stochastic process. It is, thus, of great interest to optimize the entire process as a whole rather than treat them independently or sequentially. In this paper, we take real-time display advertising as an example, where the predicted user's ad click-through rate (CTR) is employed to calculate a bid for an ad impression in the second price auction. We reformulate a common logistic regression CTR model by putting it back into its subsequent bidding context: rather than minimizing the prediction error, the model parameters are learned directly by optimizing campaign profit. The gradient update resulted from our formulations naturally fine-tunes the cases where the market competition is high, leading to a more cost-effective bidding. Our experiments demonstrate that, while maintaining comparable CTR prediction accuracy, our proposed user response learning leads to campaign profit gains as much as 78.2% for offline test and 25.5% for online A/B test over strong baselines

Entry and competition effects in first-price auctions: theory and evidence from procurement auctions

Motivated by several interesting features of the highway mowing auction data from Texas Department of Transportation (TDoT), we propose a two-stage procurement auction model with endogenous entry and uncertain number of actual bidders. Our entry and bidding models pro vide several interesting implications. For the first time, we show that even within an independent private value paradigm, as the number of potential bidders increases, bidders equilibrium bidding behavior may become less aggressive because the entry effect is always positive and may dominate the negative competition effect. We also show that it is possible that the relationship between the expected winning bid and the number of potential bidders is non-monotone decreasing as well. We then develop an empirical model of entry and bidding controlling for unobserved auction heterogeneity to analyze the data. The structural estimates are used to quantify the entry effect and the competition effect with regard to the individual bids and the procurement cost, as well as the savings for the government with regard to the procurement cost when the entry cost is reduced.

Reserve price optimization in display advertising

Display advertising is the main type of online advertising, and it comes in the form of banner ads and rich media on publishers\u27 websites. Publishers sell ad impressions, where an impression is one display of an ad in a web page. A common way to sell ad impressions is through real-time bidding (RTB). In 2019, advertisers in the United States spent nearly 60 billion U.S. dollars on programmatic digital display advertising. By 2022, expenditures are expected to increase to nearly 95 billion U.S. dollars. In general, the remaining impressions are sold directly by the publishers. The only way for publishers to control the price of the impressions they sell through RTB is by setting up a reserve price, which has to be beaten by the winning bids. The two main types of RTB auction strategies are 1) first-price auctions, i.e., the winning advertiser pays the highest bid, and 2) second-price auctions, i.e., the winning advertiser pays the maximum of the second highest bid and the reserve price (the minimum price that a publisher can accept for an impression). In both types of auctions, bids lower than the reserve prices will be automatically rejected. Since both strategies are influenced by the reserve price, setting a good reserve price is an important, but challenging task for publishers. A high reserve price may lead to very few winning bids, and thus can decrease the revenue substantially. A low reserve price may devalue the impressions and hurt the revenue because advertisers do not need to bid high to beat the reserve. Reduction of ad revenue may affect the quality of free content and publishers\u27 business sustainability. Therefore, in an ideal situation, the publishers would like to set the reserve price as high as possible, while ensuring that there is a winning bid. This dissertation proposes to use machine learning techniques to determine the optimal reserve prices for individual impressions in real-time, with the goal of maximizing publishers\u27 ad revenue. The proposed techniques are practical because they use data only available to publishers. They are also general because they can be applied to most online publishers. The novelty of the research comes from both the problem, which was not studied before, and the proposed techniques, which are adapted to the online publishing domain. For second-price auctions, a survival-analysis-based model is first proposed to predict failure rates of reserve prices of specific impressions in second-price auctions. It uses factorization machines (FM) to capture feature interaction and header bidding information to improve the prediction performance. The experiments, using data from a large media company, show that the proposed model for failure rate prediction outperforms the comparative systems. The survival-analysis-based model is augmented further with a deep neural network (DNN) to capture the feature interaction. The experiments show that the DNN-based model further improves the performance from the FM-based one. For first-price auctions, a multi-task learning framework is proposed to predict the lower bounds of highest bids with a coverage probability. The model can guarantee the highest bids of at least a certain percentage of impressions are more than the corresponding predicted lower bounds. Setting the final reserve prices to the lower bounds, the model can guarantee a certain percentage of outbid impressions in real-time bidding. The experiments show that the proposed method can significantly outperform the comparison systems

Weekend effect in internet search advertising

Journal ArticleThis paper examines whether Internet search advertising exhibits a weekend effect, a substantial difference in the effectiveness of ad spending on weekends vs on weekdays. We employ a data set from a major hotel chain, consisting of daily spending on search ads for 13 months, across three search engines and two brands. We find that there is a strong weekend effect. Each dollar of ad spending on weekends delivers a lower sales return than the corresponding return on weekdays. The advertising elasticity (percentage change in sales for a percentage change in ad spending) is about 3:7% lower on weekends, which translates to a 10% reduction in sales return at the mean level of daily spending. The weekend effect is robust across the 6 combinations of search engines and brands. We show that the reduction in advertising elasticity is primarily attributable to an increase in the price of clicks on weekends rather than due to any differences in conversion rate of click-throughs to sales. Further, we find that the weekend effect is exacerbated for ad spending at the top-ranked paid search listings. Awareness of the weekend effect can help managers fine-tune the distribution of their advertising budget across time, and achieve greater sales returns from a given ad budget