Multi Task Learning: A Survey and Future Directions

Abstract

Multi-task learning (MTL) is a problem that must be applied in modern recommendation systems and is just as difficult. In the recent e-commerce advertising market, it is necessary to be able to predict not only the probability of users clicking, but also the probability of conversion and purchase. By predicting multi-task, it is possible to increase the accuracy of each task and optimize advertisements for various goals of advertisers. Traditional conversion rate (CVR) prediction models have difficulty learning because the number of conversions is too small compared to the total number of impressions. This problem is called a data sparsity (DS) problem. Another problem is that CVR models trained with samples of clicked impressions infer on samples of all impressions. This problem is called a sample selection bias (SSB) problem. This paper is a summary of the various solutions and current limitations and further directions about solving sample selection bias problem and data sparsity problem.