Dimensionality reduction for click-through rate prediction: Dense versus sparse representation
This work addresses the need for efficient prediction in online advertising auctions with tight time constraints, representing an incremental improvement in feature engineering.
The paper tackled the problem of fast click-through rate prediction for real-time bidding by using dimensionality reduction on user-website interaction graphs, showing that the Infinite Relational Model achieves comparable performance with fewer features and faster computations than conventional methods.
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.