Stochastic Blockmodeling for Online Advertising
This work addresses the problem of improving ad-targeting and business strategies for online advertising companies, but it appears incremental as it builds on existing stochastic blockmodeling methods.
The paper tackles the challenge of analyzing voluminous, sparse, high-dimensional, and noisy online advertising data by introducing a stochastic blockmodeling approach for website relations based on user visitation events, and demonstrates its effectiveness through simulation and an AOL dataset.
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.