Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

By Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018

540 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/28e73fe5a3ca2891fa04dbf9fcc6ab4965a68182

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Yong Li
4. Fuli Feng
5. Chen Gao
6. Meng Wang
7. Depeng Jin
8. Tat-seng Chua

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Fuli Feng
4. Meng Wang
5. Yong Li
6. Chen Gao
7. Depeng Jin
8. Tat-seng Chua

In Management Science and Operations Research

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Fuli Feng
5. Jiawei Chen
6. De-chuan Zhan
7. Hande Dong
8. Yong Li

Citations in the corpus

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “Some works [122] propose to optimize the click-through rate and conversion rate at the same time.”
• Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling (2020, methodology): “The delayed feedback in the bandit algorithm has been researched (Joulani, György, and Szepesvári 2013; Mandel et al. 2015; Cesa-Bianchi, Gentile, and Mansour 2019).”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Some recent work also consider users’ sequential behaviors “exposure-click-conversion” and correspondingly devise an inverse propensity model [81], decomposition model [82], [83] or graph neural network [84] on the sequential behaviors to address exposure bias with multi-task learning.”