1. DeepBET Reverse-Engineering the Behavioral Targeting mechanisms of Ad Networks via Deep Learning Sotirios Chatzis Cyprus University of Technology

2. Motivation The capability to analyze and reverse-engineer the behavioral targeting practices of online ad networks is a significant challenge. Existing approaches suffer from specific limitations: o They collect data via artificial user profiles pertaining to single interest types. However, real-world users may have diverse interest type combinations; users with multiple interests may be presented with a set of ads different from the union of the sets of ads pertaining to their individual interests. o User interests may change over time. Existing approaches do not examine whether the output of an ad network depends only on the most recently visited website, or on long temporal dynamics.

3. Our approach Resolves these two issues by: 1.Efficiently analyzing a very large subspace of the user behavioral patterns space. 2.Employing inference algorithms capable of capturing underlying temporal dynamics. To this end, artificial user profile creation comprises: 1.Intelligent selection of websites to populate user profiles with. 2.Creation of website sequences that represent very diverse user behavioral patterns.

4. Website Selection Based on the top 100 websites of various Adwords Ad Planner categories. System selects a subset of them by: 1.Utilizing a Deep Boltzmann machine (DBM) to generate high-level website content representations in an unsupervised fashion. 2.Presenting them to a nonparametric Bayesian hierarchical clustering model, namely a Bayesian Rose Tree (BRT). BRT infers both the number of clusters and their hierarchy (tree). 3.Retaining representatives of clusters at only some tree layer(s).

5. Profile Creation 1.Creates thousands of random sequences comprising the retained representative items. 2.Employs an algorithm for (unsupervised) extraction of high-level representations from these sequential data, namely Long Short- Term Memory Auto-Encoders (LSTM-AE). 3.Clusters the so-obtained representations via a BRT model. 4.Retains representative sequences at only some tree layer(s). Each retained (cluster) representative is utilized as a user profile to query ad nets with.

6. Inference The selected website sequences constitute a small set that comprises highly diverse user behavioral patterns. Thus, this set allows for effectively and efficiently exploring the space of possible user behavioral patterns, so as to robustly infer ad network behavioral targeting mechanisms. We resort to the dynamic Bayesian Probabilistic Matrix Factorization (dBPMF) algorithm. It infers: o The correlations between interest type combinations and ad network outputs. o How temporal dynamics affect the generated outputs.

7. Questions? Q&A after lunch…