Modeling user purchase behavior is a critical challenge in display advertising systems, necessary for real-time bidding. The difficulty arises from the sparsity of positive user events and the stochasticity of user actions, leading to severe class imbalance and irregular event timing. Predictive systems usually rely on hand-crafted "counter" features, overlooking the fine-grained temporal evolution of user intent. Meanwhile, current sequential models extract direct sequential signal, missing useful event-counting statistics. We enhance deep sequential models with self-supervised pretraining strategies for display advertising. Especially, we introduce Abacus, a novel approach of predicting the empirical frequency distribution of user events. We further propose a hybrid objective unifying Abacus with sequential learning objectives, combining stability of aggregated statistics with the sequence modeling sensitivity. Experiments on two real-world datasets show that Abacus pretraining outperforms existing methods accelerating downstream task convergence, while hybrid approach yields up to +6.1% AUC compared to the baselines.

The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse objectives of advertisers. In this paper we consider the problem at the level of user timelines instead of individual bid requests, manipulating full policies (i.e. pre-defined bidding strategies) and not bid values. In order to optimally allocate policies to users, typical multiple treatments allocation methods solve knapsack-like problems which aim at maximizing an expected value under constraints. In the specific context of online advertising, we argue that optimizing for the probability of success is a more suited objective than expected value maximization, and we introduce the SuccessProbaMax algorithm that aims at finding the policy allocation which is the most likely to outperform a fixed reference policy. Finally, we conduct comprehensive experiments both on synthetic and real-world data to evaluate its performance. The results demonstrate that our proposed algorithm outperforms conventional expected-value maximization algorithms in terms of success rate.

In this paper, we consider the task of ranking individuals based on the potential benefit of being "treated" (e.g. by a drug or exposure to recommendations or ads), referred to as Uplift Modeling in the literature. This application has gained a surge of interest in recent years and it is found in many applications such as personalized medicine, recommender systems or targeted advertising. In real life scenarios the capacity of models to rank individuals by potential benefit is measured by the Area Under the Uplift Curve (AUUC), a ranking metric related to the well known Area Under ROC Curve. In the case where the objective function, for learning model parameters, is different from AUUC, the capacity of the resulting system to generalize on AUUC is limited. To tackle this issue, we propose to learn a model that directly optimizes an upper bound on AUUC. To find such a model we first develop a generalization bound on AUUC and then derive from it a learning objective called AUUC-max, usable with linear and deep models. We empirically study the tightness of this generalization bound, its effectiveness for hyperparameters tuning and show the efficiency of the proposed learning objective compared to a wide range of competitive baselines on two classical uplift modeling benchmarks using real-world datasets.