Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Sampling RTB transactions in an online machine learning setting

We (the machine learning team at Jampp) strive to predict click-through rates (CTR) and conversion rates (CVR) for the real-time bidding (RTB) online advertising market by means of an in-house online machine learning platform based on a state-of-the-art stochastic gradient descent estimator. Our estimation framework has already been covered in a previous paper, so here we want to focus on some peripheral aspects of our platform that, in spite of being of a somewhat ancillary nature, nevertheless tend to dominate development efforts and overall system complexity; namely, in order to feed the learning system we first need to sample a very high-volume stream of out-of-order and scattered-in-time events and consolidate them into a sequence of observations representing the underlying market transactions, each observation composed of a set of features and a response, from which the estimator is ultimately able to learn. This paper is written in a down-to-earth fashion: we describe a number of particular difficulties the general problem of sampling in an online high-volume setting poses and then we present our concrete answers to those difficulties based on real, hands-on, experience.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes

In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint. This paper presents a bid recommendation scenario that discovers the concavity changes in click prediction curves. The recommended bid is derived based on the turning point from significant increase (i.e. concave downward) to slow increase (convex upward). Parametric learning based method is applied by solving the corresponding constraint optimization problem. Empirical studies on real-world advertising scenarios clearly demonstrate the performance gains for business metrics (including revenue increase, click increase and advertiser ROI increase).Comment: 10 page

Bankruptcy Law

This article will cover both consumer and business bankruptcy issues, and is limited primarily to decisions by courts within the Fourth Circuit since mid-2012. Despite these general parameters, because bankruptcy is federal law, there are some cases outside the Fourth Circuit that are included due to their influential and instructive nature. The intention of this update is to provide bankruptcy practitioners in Virginia with concise, yet compre-hensive, case summaries that will prove to be a valuable researchtool

Convex Optimization, Stochastic Approximation, and Optimal Contract Management in Real-time Bidding

This thesis studies problems at the intersection of monotone and convex optimization, auction theory, and electronic commerce. Convex optimization and the theory of stochastic approximation serve as the basic practical and theoretical tools we have drawn upon. We solve important problems facing Demand Side Platforms (DSPs) and other demand aggregators (to be defined in the main body) in the e-commerce space, particularly in the field of real-time bidding (RTB). RTB is a real-time auction market, the primary application of which is the selling advertising space. Our main contribution to this field, at its most basic, is to recognize that certain optimal bidding problems can be re-cast as convex optimization problems. Particular focus will be placed upon the second price auction mechanism due to the strikingly simple structural results that hold in this case; but many results generalize to the first price auction mechanism under additional assumptions. We will also touch upon formal connections between these auction problems and two important problems in finance, namely the dark pool problem, and optimal portfolio construction