The Pascal Exploration & Exploitation Challenge seeks to improve the relevance of content presented to visitors of a website, based on their individual interests.


In this challenge, the submitted algorithms have to predict which visitors of a website are likely to click on which piece of content. Visitors are characterised by a set of 120 features. Predicting clicks accurately, based on these features, is essential to present content that is relevant to visitors’ interests. It requires to continuously learn what might be of interest (exploration), while using this learning to serve relevant content often enough (exploitation).


Algorithms are run online (i.e. they receive their input sequentially) on data provided by Adobe Omniture, which closely simulates an actual web campaign. Each visitor click gives a reward of 1, and the best algorithm is the one that has highest cumulated reward in the end. The challenge will be run in phases, in between which the participants will have the opportunity to update their algorithms based on previous observations.



From its experience in web analytics, Adobe Omniture has created a dataset that simulates the responses to a web campaign, with changes over time. The dataset comprises about 20 million {visitor feature vector, option id, binary clickthrough indicator} records that each represent a single visit to the website. For each visitor feature vector v of the data set, and for each option i, the binary clickthrough indicator informs us on whether v would click on i or not.