Open AccessPosted Content
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
Jun Wang,Weinan Zhang,Shuai Yuan +2 more
TLDR
Topics covered include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimization, statistical arbitrage, dynamic pricing, and ad fraud detection are an invaluable text for researchers and practitioners alike.Abstract:
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.read more
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Mean Field Multi-Agent Reinforcement Learning
TL;DR: In this paper, a mean field Q-learning and mean field Actor-Critic algorithms are proposed to solve the Ising model via model-free reinforcement learning methods. But the authors admit that the learning of the individual agent's optimal policy depends on the dynamics of the population, while the dynamics change according to the collective patterns of individual policies.
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Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
TL;DR: This paper introduces a Multiagent Bidirectionally-Coordinated Network (BiCNet) with a vectorised extension of actor-critic formulation and demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players.
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Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games.
TL;DR: This analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of coordination strategies that is similar to these of experienced game players, and is easily adaptable to the tasks with heterogeneous agents.
Journal ArticleDOI
Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics
TL;DR: A comprehensive literature review of major scientific contributions made so far in this research area is undertaken and a holistic overview of the main applications of Data Sciences to digital marketing is presented to generate insights related to the creation of innovative Data Mining and knowledge discovery techniques.
Proceedings ArticleDOI
Real-Time Bidding by Reinforcement Learning in Display Advertising
TL;DR: In this article, the authors formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set.
References
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