A banner recommendation system based on web navigation history

We address the problem of selecting a banner advertisement, based on the profile of the online user. The profile consists of the set of webpages opened by the online user, optionally clustered. In order to select the banner, we train a classifier with a dataset containing rules of the form P(u) → B(u), where P(u) is the profile of user u, and B(u) is the set of banners clicked by user u. We present two possible transformations that we use in order to train the classifier. In the first transformation, TMax, we put only one line P(u) →b, where b is the most frequently clicked banner by the user u. In the second transformation, TMultiline, we put one line P(u) → b, for each banner b in B(u). Finally, we perform several experiments, which show that there is a strong correlation between the profiles of the user, and the banners clicked by the user.