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

Bjarne Ørum Fruergaard, Toke Jansen Hansen, Lars Kai Hansen

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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.
Original languageEnglish
Title of host publicationProceedings of the 3rd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging 2013
Number of pages13
Publication date2013
Publication statusPublished - 2013
Event27th Annual Conference on Neural Information Processing Systems (NIPS 2013) - Lake Tahoe, Nevada, United States
Duration: 5 Dec 201310 Dec 2013
http://nips.cc/Conferences/2013/

Conference

Conference27th Annual Conference on Neural Information Processing Systems (NIPS 2013)
CountryUnited States
CityLake Tahoe, Nevada
Period05/12/201310/12/2013
Internet address

Bibliographical note

Presented on the Probabilistic Models for Big Data workshop at NIPS 2013.

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