Extreme Multi-Label Classification for Ad Targeting Using Factorization Machines

Applications involving Extreme Multi-Label Classification (XMLC) face several practical challenges with respect to scale, model size and prediction latency, while maintaining satisfactory predictive accuracy. In this paper, we propose a Multi-Label Factorization Machine (MLFM) model, which addresses some of the challenges in XMLC problems. We use behavioral ad targeting as a case study to illustrate the benefits of the MLFM model. Predicting user qualifications for targeting segments plays a major role in both personalization and real-time bidding. Considering the large number of segments and the prediction time requirements of real-world production systems, building scalable models is often difficult and computationally burdensome. To cope with these challenges, we (1) reformulate the problem of assigning users to segments as a multi-label classification (XMLC) problem, and (2) leverage the benefits of the conventional FM model and generalize its capacity to joint prediction across a large number of targeting segments. We have shown that the MLFM model is both effective and computationally efficient compared to several baseline models on publicly available datasets in addition to the targeting use case.