Stochastic Blockmodeling for Online Advertising

TL;DR

This paper introduces stochastic blockmodeling techniques to analyze website relations in online advertising, enabling better understanding of website structures for improved targeting and revenue strategies.

Contribution

It proposes two novel clustering algorithms for website structure discovery and compares their performance with existing methods using real and simulated data.

Findings

Algorithms effectively identify intrinsic website structures

Clustering methods outperform traditional partitioning approaches

Demonstrated success on AOL dataset and simulations

Abstract

Online advertising is an important and huge industry. Having knowledge of the website attributes can contribute greatly to business strategies for ad-targeting, content display, inventory purchase or revenue prediction. Classical inferences on users and sites impose challenge, because the data is voluminous, sparse, high-dimensional and noisy. In this paper, we introduce a stochastic blockmodeling for the website relations induced by the event of online user visitation. We propose two clustering algorithms to discover the instrinsic structures of websites, and compare the performance with a goodness-of-fit method and a deterministic graph partitioning method. We demonstrate the effectiveness of our algorithms on both simulation and AOL website dataset.