A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search

Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature that investigate how to design an auction mechanism in order to optimize the revenue of the search engine. However, due to some unrealistic assumptions used, the practical values of these studies are not very clear. In this paper, we propose a novel \emph{game-theoretic machine learning} approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimization framework. In particular, we first learn a Markov model from historical data to describe how advertisers change their bids in response to an auction mechanism, and then for any given auction mechanism, we use the learnt model to predict its corresponding future bid sequences. Next we learn the auction mechanism through empirical revenue maximization on the predicted bid sequences. We show that the empirical revenue will converge when the prediction period approaches infinity, and a Genetic Programming algorithm can effectively optimize this empirical revenue. Our experiments indicate that the proposed approach is able to produce a much more effective auction mechanism than several baselines.Comment: Twenty-third International Conference on Artificial Intelligence (IJCAI 2013

Theoretical Prediction and Optimization Approach to Triboelectric Nanogenerator

Triboelectric nanogenerator (TENG) is a new type of electrostatic generator based on the principle of Maxwell displacement current. It could be designed as a device for either smart sensing or energy harvesting via converting mechanical energy into electric power efficiently. To predict its output characteristic, investigate its working mechanism, and enhance its working performance, the theoretical analysis and optimization work in either experimental or theoretical means are of great significance. In this chapter, we plan to introduce the progress of theoretical analysis and optimization approach to TENG with four different modes. Three parts of work will be introduced in the manuscript: (1) the theoretical prediction approach for electric output performance of TENG device, (2) the optimization strategies for TENG device based on figure of merits, and (3) the scaling laws between the normalized electric outputs and multiple physical properties of the TENG device