Dimensionality reduction for click-through rate prediction: Dense versus sparse representation

TL;DR

This paper explores using the Infinite Relational Model for dimensionality reduction in click-through rate prediction, demonstrating it offers comparable accuracy to traditional methods but with faster computation and fewer features, ideal for real-time bidding.

Contribution

The paper introduces IRM-based features for CTR prediction, showing they are efficient and effective for fast, real-time online advertising applications.

Findings

IRM provides similar predictive performance to conventional methods.

IRM achieves faster computation and uses fewer features.

IRM-based features are suitable for real-time bidding environments.

Abstract

In online advertising, display ads are increasingly being placed based on real-time auctions where the advertiser who wins gets to serve the ad. This is called real-time bidding (RTB). In RTB, auctions have very tight time constraints on the order of 100ms. Therefore mechanisms for bidding intelligently such as clickthrough rate prediction need to be sufficiently fast. In this work, we propose to use dimensionality reduction of the user-website interaction graph in order to produce simplified features of users and websites that can be used as predictors of clickthrough rate. We demonstrate that the Infinite Relational Model (IRM) as a dimensionality reduction offers comparable predictive performance to conventional dimensionality reduction schemes, while achieving the most economical usage of features and fastest computations at run-time. For applications such as real-time bidding, where fast database I/O and few computations are key to success, we thus recommend using IRM based features as predictors to exploit the recommender effects from bipartite graphs.