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

by   Bjarne Ørum Fruergaard, et al.

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.


page 1

page 2

page 3

page 4


Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study

Affective computing has become a very important research area in human-m...

Dimensionality Reduction with Subspace Structure Preservation

Modeling data as being sampled from a union of independent subspaces has...

A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations

This paper gives a review and synthesis of methods of evaluating dimensi...

"Why Here and Not There?" – Diverse Contrasting Explanations of Dimensionality Reduction

Dimensionality reduction is a popular preprocessing and a widely used to...

SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality Reduction

The most effective dimensionality reduction procedures produce interpret...

Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations

We demonstrate that almost all non-parametric dimensionality reduction m...

Please sign up or login with your details

Forgot password? Click here to reset