Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representations, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two conversion types, and the weighted AUC across all conversion types is also improved by 0.50%.Comment: SIGKD

Learning Classifiers under Delayed Feedback with a Time Window Assumption

We consider training a binary classifier under delayed feedback (DF Learning). In DF Learning, we first receive negative samples; subsequently, some samples turn positive. This problem is conceivable in various real-world applications such as online advertisements, where the user action takes place long after the first click. Owing to the delayed feedback, simply separating the positive and negative data causes a sample selection bias. One solution is to assume that a long time window after first observing a sample reduces the sample selection bias. However, existing studies report that only using a portion of all samples based on the time window assumption yields suboptimal performance, and the use of all samples along with the time window assumption improves empirical performance. Extending these existing studies, we propose a method with an unbiased and convex empirical risk constructed from the whole samples under the time window assumption. We provide experimental results to demonstrate the effectiveness of the proposed method using a real traffic log dataset