The Pascal Exploration & Exploitation challenge 2011 seeks to evaluate algorithms for the online website content selection problem. This article presents the solution we used to achieve second place in this challenge and some side-experiments we performed. The methods we evaluated are all structured in three layers. The rst layer provides an online summary of the data stream for continuous and nominal data. Continuous data are handled using an online quantile summary. Nominal data are summarized with a hash-based counting structure. With these techniques, we managed to build an accurate stream summary with a small memory footprint. The second layer uses the summary to build predictors. We exploited several kinds of trees from simple decision stumps to deep multivariate ones. For the last layer, we explored several combination strategies: online bagging, exponential weighting, linear ranker, and simple averaging.