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

Developing a web-based cellular automata model for urban growth simulation

Cellular automata as an emerging technology have been adapted increasingly by geographers and planners to simulate the spatial and temporal processes of urban growth. While the literature reports many applications of cellular automata models for urban studies, in practice, the operation of the models as well as the configuration and calibration of relevant parameters used in the models were only known to the model builders. This is largely due to the constraint that most cellular automata models were developed based on desktop computer programs, either by incorporating the model within a desktop GIS environment, or developing the model independent of a desktop GIS. Consequently, there is little input from the user to test or visualise the actual operation or evaluate the applicability of the model under different conditions. This paper presents a methodology to implement a fuzzy constrained cellular automata model of urban growth within a web-based GIS environment, using the actual urban growth of Metropolitan Sydney, Australia from 1976 to 2006 as a case study. With the web-based cellular automata model, users can visualise and test the operation of the model; they can also modify or calibrate the model's parameters to evaluate its simulation accuracies, or even feed the model with various 'what-if' conditions to generate alterative outcomes. Such a web-based modelling platform provides a useful and effective channel for government authority and stakeholders to evaluate different urban growth scenarios. It also provides an interactive environment that can foster public participation in urban planning and management