In display ad auctions of Real-Time Bidding (RTB), a typical Demand-Side Platform (DSP) bids based on the predicted probability of click and conversion right after an ad impression. Recent studies find such a strategy is suboptimal and propose a better bidding strategy named lift-based bidding. Lift-based bidding simply bids the price according to the lift effect of the ad impression and achieves maximization of target metrics such as sales. Despite its superiority, lift-based bidding has not yet been widely accepted in the avertising industry. For one reason, lift-based bidding is less profitable f or DSP providers under the current billing rule. Second, the practical usefulness of lift-based bidding is not widely understood in the online advertising industry due to the lack of a comprehensive investigation of its impact.We here propose a practically-implementable lift-based bidding system that perfectly fits the current billing rules. We conduct extensive experiments using a real-world advertising campaign and examine the performance under various settings. We find that lift-based bidding, especially unbiased lift-based bidding is most profitable for both DSP providers and advertisers. Our ablation study highlights that lift-based bidding has a good property for currently dominant first price auctions. The results will motivate the online advertising industry to consider lift-based advertising.

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.