  Estimating post-click conversion rate (CVR) accurately is crucial for ranking
systems in industrial applications such as recommendation and advertising.
Conventional CVR modeling applies popular deep learning methods and achieves
state-of-the-art performance. However it encounters several task-specific
problems in practice, making CVR modeling challenging. For example,
conventional CVR models are trained with samples of clicked impressions while
utilized to make inference on the entire space with samples of all impressions.
This causes a sample selection bias problem. Besides, there exists an extreme
data sparsity problem, making the model fitting rather difficult. In this
paper, we model CVR in a brand-new perspective by making good use of sequential
pattern of user actions, i.e., impression -> click -> conversion. The proposed
Entire Space Multi-task Model (ESMM) can eliminate the two problems
simultaneously by i) modeling CVR directly over the entire space, ii) employing
a feature representation transfer learning strategy. Experiments on dataset
gathered from Taobao's recommender system demonstrate that ESMM significantly
outperforms competitive methods. We also release a sampling version of this
dataset to enable future research. To the best of our knowledge, this is the
first public dataset which contains samples with sequential dependence of click
and conversion labels for CVR modeling.
