Optimizing trade-offs among stakeholders in real-time bidding by incorporating multimedia metrics

Displaying banner advertisements (in short, ads) on webpages has usually been discussed as an Internet economics topic where a publisher uses auction models to sell an online user's page view to advertisers and the one with the highest bid can have her ad displayed to the user. This is also called \emph{real-time bidding} (RTB) and the ad displaying process ensures that the publisher's benefit is maximized or there is an equilibrium in ad auctions. However, the benefits of the other two stakeholders -- the advertiser and the user -- have been rarely discussed. In this paper, we propose a two-stage computational framework that selects a banner ad based on the optimized trade-offs among all stakeholders. The first stage is still auction based and the second stage re-ranks ads by considering the benefits of all stakeholders. Our metric variables are: the publisher's revenue, the advertiser's utility, the ad memorability, the ad click-through rate (CTR), the contextual relevance, and the visual saliency. To the best of our knowledge, this is the first work that optimizes trade-offs among all stakeholders in RTB by incorporating multimedia metrics. An algorithm is also proposed to determine the optimal weights of the metric variables. We use both ad auction datasets and multimedia datasets to validate the proposed framework. Our experimental results show that the publisher can significantly improve the other stakeholders' benefits by slightly reducing her revenue in the short-term. In the long run, advertisers and users will be more engaged, the increased demand of advertising and the increased supply of page views can then boost the publisher's revenue

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

The Allocation of European Union Allowances: Lessons, Unifying Themes and General Principles

This paper is the concluding chapter of Rights, Rents and Fairness: Allocation in the European Emissions Trading Scheme, edited by the co-authors and forthcoming from Cambridge University Press. The main objective of this paper is to distill the lessons and general principles to be learnt from the allocation of allowances in the European Union Emission Trading Scheme (EU ETS), i.e. in the world’s first experience with allocating carbon allowances to sub-national entities. We discuss the lessons that emerge from this experience and make some comments on what seem to be more general principles informing the allocation process and on what are the global implications of the EU ETS. As has become obvious during the first allocation phase, the diversity of experience among the Member States is considerable, so that it must be understood that these lessons and unifying themes are drawn from the experience of most of the Member States, not necessarily from all. Lessons and unifying observations are grouped in three categories: those concerning the conditions encountered, the processes employed, and the actual choices.Climate Change, Emission Trading, Allocation, Fairness, EU Policy

ICIS Panel Summary: Should Institutional Trust Matter in Information Systems Research?

This paper summarizes and expands the panel on Should Institutional Trust Matter in Information Systems Research? that was presented during the ICIS 2005 Conference in Las Vegas. The panel was co-chaired by Paul A. Pavlou of the University of California and by David Gefen of Drexel University. The panelists were Izak Benbasat of the University of British Columbia, Harrison McKnight of Michigan State University, Katherine Stewart of the University of Maryland, and Detmar W. Straub of Georgia State University. There were about 150 people attending the panel and taking part in the lively discussion that pursued. Due to the interest the panel aroused, this paper expands on the topics discussed and presents them in a much broader perspective in a set of appendices

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

Automated Negotiation Among Web Services

Software as a service is well accepted software deployment and distribution model that is grown exponentially in the last few years. One of the biggest benefits of SaaS is the automated composition of these services in a composite system. It allows users to automatically find and bind these services, as to maximize the productivity of their composed systems, meeting both functional and non-functional requirements. In this paper we present a framework for modeling the dependency relationship of different Quality of Service parameters of a component service. Our proposed approach considers the different invocation patterns of component services in the system and models the dependency relationship for optimum values of these QoS parameters. We present a service composition framework that models the dependency relations ship among component services and uses the global QoS for service selection

Bidding Markets

The existence of a ‘bidding market’ is commonly cited as a reason to tolerate the creation or maintenance of highly concentrated markets. We discuss three erroneous arguments to that effect: the ‘consultants’ fallacy’ that ‘market power is impossible’, the ‘academics’ fallacy’ that (often) ‘market power does not matter’, and the ‘regulators’ fallacy’ that ‘intervention against pernicious market power is unnecessary’, in markets characterized by auctions or bidding processes. Furthermore we argue that the term ‘bidding market’ as it is widely used in antitrust is unhelpful or misleading. Auctions and bidding processes do have some special features—including their price formation processes, common-values behaviour, and bid-taker power—but the significance of these features has been overemphasized, and they often imply a need for stricter rather than more lenient competition policy.Bidding Markets, Auctions, Antitrust, Competition Policy, Bidding, Market Power, Private Values, Common Values, Anti-trust