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

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