Topics in Computational Advertising

Computational advertising is an emerging scientific discipline that incorporates tools and ideas from fields such as statistics, computer science, and economics. Although a consequence of the rapid growth of the Internet, computational advertising has since helped transform the online advertising business into a multi-billion dollar industry.

The fundamental goal of computational advertising is to determine the ``best'' online ad to display to any given user. This ``best'' ad, however, changes depending upon the specific context that is under consideration. This leads to a variety of different problems, three of which are discussed in this thesis.

Chapter 1 briefly introduces the topics of online advertising and computational advertising. Chapter 2 proposes a numerical method to approximate the pure strategy Nash equilibrium bidding functions in an independent private value first-price sealed-bid auction where bidders draw their types from continuous and atomless distributions---a setting in which solutions cannot generally be analytically derived, despite the fact that they are known to exist and be unique. Chapter 3 proposes a cross-domain recommender system that is a multiple-domain extension of the Bayesian Probabilistic Matrix Factorization model. Chapter 4 discuss some of the tools and challenges of text mining by using the Trayvon Martin shooting incident as a case study in analyzing the lexical content and network connectivity structure of the political blogosphere. Finally, Chapter 5 presents some concluding remarks and briefly discusses other problems in computational advertising.