Scalable supervised dimensionality reduction using clustering

Authors:
Troy Raeder;Claudia Perlich;Brian Dalessandro;Ori Stitelman;Foster Provost
Affiliations:
m6d Research, New York, USA;m6d Research, New York, USA;m6d Research, New York, USA;m6d Research, New York, USA;New York University, New York, USA
Venue:
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2013

Citing 16
Cited 0

Stacked regressions

Machine Learning
Fast supervised dimensionality reduction algorithm with applications to document categorization & retrieval

Proceedings of the ninth international conference on Information and knowledge management
Ensembles of Learning Machines

WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Distribution-based aggregation for relational learning with identifier attributes

Machine Learning
Constructing informative priors using transfer learning

ICML '06 Proceedings of the 23rd international conference on Machine learning
Convex multi-task feature learning

Machine Learning
Feature hashing for large scale multitask learning

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Audience selection for on-line brand advertising: privacy-friendly social network targeting

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Matrix Factorization Techniques for Recommender Systems

Computer
Estimating rates of rare events with multiple hierarchies through scalable log-linear models

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A Survey on Transfer Learning

IEEE Transactions on Knowledge and Data Engineering
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions

SIAM Review
Supervised nonlinear dimensionality reduction for visualization and classification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks
Bid optimizing and inventory scoring in targeted online advertising

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Design principles of massive, robust prediction systems

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

The automated targeting of online display ads at scale requires the simultaneous evaluation of a single prospect against many independent models. When deciding which ad to show to a user, one must calculate likelihood-to-convert scores for that user across all potential advertisers in the system. For modern machine-learning-based targeting, as conducted by Media6Degrees (M6D), this can mean scoring against thousands of models in a large, sparse feature space. Dimensionality reduction within this space is useful, as it decreases scoring time and model storage requirements. To meet this need, we develop a novel algorithm for scalable supervised dimensionality reduction across hundreds of simultaneous classification tasks. The algorithm performs hierarchical clustering in the space of model parameters from historical models in order to collapse related features into a single dimension. This allows us to implicitly incorporate feature and label data across all tasks without operating directly in a massive space. We present experimental results showing that for this task our algorithm outperforms other popular dimensionality-reduction algorithms across a wide variety of ad campaigns, as well as production results that showcase its performance in practice.