You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction

In the cost per click (CPC) pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers may be unsatisfied with ad networks charging them for "valueless" clicks, or so-called accidental clicks. [...] Charging advertisers for such clicks is detrimental in the long term as the advertiser may decide to run their campaigns on other ad networks. In addition, machine-learned click models trained to predict which ad will bring the highest revenue may overestimate an ad click-through rate, and as a consequence negatively impacting revenue for both the ad network and the publisher. In this work, we propose a data-driven method to detect accidental clicks from the perspective of the ad network. We collect observations of time spent by users on a large set of ad landing pages - i.e., dwell time. We notice that the majority of per-ad distributions of dwell time fit to a mixture of distributions, where each component may correspond to a particular type of clicks, the first one being accidental. We then estimate dwell time thresholds of accidental clicks from that component. Using our method to identify accidental clicks, we then propose a technique that smoothly discounts the advertiser's cost of accidental clicks at billing time. Experiments conducted on a large dataset of ads served on Yahoo mobile apps confirm that our thresholds are stable over time, and revenue loss in the short term is marginal. We also compare the performance of an existing machine-learned click model trained on all ad clicks with that of the same model trained only on non-accidental clicks. There, we observe an increase in both ad click-through rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when using the latter. [...

REAL-TIME AD CLICK FRAUD DETECTION

With the increase in Internet usage, it is now considered a very important platform for advertising and marketing. Digital marketing has become very important to the economy: some of the major Internet services available publicly to users are free, thanks to digital advertising. It has also allowed the publisher ecosystem to flourish, ensuring significant monetary incentives for creating quality public content, helping to usher in the information age. Digital advertising, however, comes with its own set of challenges. One of the biggest challenges is ad fraud. There is a proliferation of malicious parties and software seeking to undermine the ecosystem and causing monetary harm to digital advertisers and ad networks. Pay-per-click advertising is especially susceptible to click fraud, where each click is highly valuable. This leads advertisers to lose money and ad networks to lose their credibility, hurting the overall ecosystem. Much of the fraud detection is done in offline data pipelines, which compute fraud/non-fraud labels on clicks long after they happened. This is because click fraud detection usually depends on complex machine learning models using a large number of features on huge datasets, which can be very costly to train and lookup. In this thesis, the existence of low-cost ad click fraud classifiers with reasonable precision and recall is hypothesized. A set of simple heuristics as well as basic machine learning models (with associated simplified feature spaces) are compared with complex machine learning models, on performance and classification accuracy. Through research and experimentation, a performant classifier is discovered which can be deployed for real-time fraud detection

Machine Learning Prediction System Based on Tensor-Flow Deep Neural Network and its Application to Advertising in Mobile Gaming

This disclosure analyzes the background of the industry and proposes solutions to the challenges faced by complex applications or games, such as strategy games. From the perspective of the system architecture, this disclosure describes how to clean up data, identify salient features, model predictive classifiers, and automate analysis and selection for content delivery. Data collection and processing have a great influence on the accuracy and applicability of the model. Four kinds of behavioral parameters (or more) may be used to predict conversion events, with PCA used to reduce the dimensions utilized as inputs to the model. In addition, by adjusting the threshold of the predicted conversion probability, a trade-off can be made between accuracy and breadth, so that the prediction results of the model can be applied to different fields

Lift-Based Bidding in Ad Selection

Real-time bidding (RTB) has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of how it can lead to a desired action event (e.g., registering an account or placing a purchase order) to the advertiser. However, this industry standard approach to decide the bid price does not consider the actual effect of the ad shown to the user, which should be measured based on the performance lift among users who have been or have not been exposed to a certain treatment of ads. In this paper, we propose a new bidding strategy and prove that if the bid price is decided based on the performance lift rather than absolute performance value, advertisers can actually gain more action events. We describe the modeling methodology to predict the performance lift and demonstrate the actual performance gain through blind A/B test with real ad campaigns in an industry-leading Demand-Side Platform (DSP). We also discuss the relationship between attribution models and bidding strategies. We prove that, to move the DSPs to bid based on performance lift, they should be rewarded according to the relative performance lift they contribute.Comment: AAAI 201

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

Online Model Evaluation in a Large-Scale Computational Advertising Platform

Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing message for a user and to purchase impressions at the right price, we rely heavily on bid prediction and optimization models. Even though the bid prediction models are well studied in the literature, the equally important subject of model evaluation is usually overlooked. Effective and reliable evaluation of an online bidding model is crucial for making faster model improvements as well as for utilizing the marketing budgets more efficiently. In this paper, we present an experimentation framework for bid prediction models where our focus is on the practical aspects of model evaluation. Specifically, we outline the unique challenges we encounter in our platform due to a variety of factors such as heterogeneous goal definitions, varying budget requirements across different campaigns, high seasonality and the auction-based environment for inventory purchasing. Then, we introduce return on investment (ROI) as a unified model performance (i.e., success) metric and explain its merits over more traditional metrics such as click-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss commonly used evaluation and metric summarization approaches in detail and propose a more accurate method for online evaluation of new experimental models against the baseline. Our meta-analysis-based approach addresses various shortcomings of other methods and yields statistically robust conclusions that allow us to conclude experiments more quickly in a reliable manner. We demonstrate the effectiveness of our evaluation strategy on real campaign data through some experiments.Comment: Accepted to ICDM201

A comparison of data-driven approaches for mobile marketing user conversion prediction

In this paper, we perform an exploratory study of user Conversion Rate (CVR) prediction using recent big data from a global mobile marketing company. We design a stream processing engine to collect sampled mobile marketing data. Then, we execute a large set of CVR prediction tests, under a two-stage experimental procedure that considers a rolling window evaluation. First, several preprocessing and machine learning combinations are analyzed using preliminary data. Next, the se- lected combinations are tested on a larger set of unseen datasets. Interesting classification performances were achieved, with some learning models (e.g., XGboost, Logistic Regression) requiring a reduced computational effort, thus showing a potential value for user CVR prediction in this domain.This article is a result of the project NORTE-01-0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundação para a Ciência e Tecnologia within the Project ˆScope: UID/CEC/00319/2013