A Priceperattention Auction Scheme Using Mouse Cursor Information Payments in online ad auctions are typically derived from clickthrough rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just clicks. That is, for example, if they aim to raise brand awareness or visibility. There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed. This article contributes a novel auction format based on a payperattention (PPA) scheme. We show that the PPA auction inherits the desirable properties (strategyproofness and efficiency) as its payperimpression and payperclick counterparts, and that it also compares favourably in terms of revenues. To make the PPA format feasible, we also contribute a scalable diagnostic technology to predict user attention to ads in sponsored search using raw mouse cursor coordinates only, regardless of the page content and structure. We use the user attention predictions in numerical simulations to evaluate the PPA auction scheme. Our results show that, in relevant economic settings, the PPA revenues would be strictly higher than the existing auction payment schemes.

Know Your Enemies and Know Yourself in the RealTime Bidding Function Optimisation Realtime bidding (RTB) is a popular method to sell online ad space inventory using realtime auctions to determine which advertiser gets to make the ad impression. Advertisers can take user information into account when making their bids and get more control over the process. The goal of an optimal bidding function is to maximise the overall effectiveness of the ad campaigns defined by the advertisers under a certain budget constraint. A straightforward solution would be to model the bidding function in an explicit form. However, such functional solutions lack generality in practice and are insensitive to the stochastic behaviour of other bidders in the environment. In this paper, we propose to formulate the online auctions into a general mean field multiagent framework, in which the agents compete with each other and each agentu0027s best response strategy depends on its opponentsu0027 actions. We firstly introduce a novel Deep Attentive Survival Analysis (DASA) model to estimate the opponentu0027s action distribution on the ad impression level which outperforms stateoftheart survival analysis. Furthermore, we introduce the DASA model as the opponent model into the Mean Field Deep Deterministic Policy Gradients (DDPG) algorithm for each agent to learn the optimal bidding strategy and converge to the mean field equilibrium. The experiments have shown that with the inference of the market, the market converges to the equilibrium faster while playing against both fixed strategy agents and dynamic learning agents.