The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Open accessAbstract
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.
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Wide & Deep Learning for Recommender Systems
Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., et al. (2016) · Proceedings of the 1st Workshop on Deep Learning for Recommender Systems · doi:10.1145/2988450.2988454 · Cited by 3,535
An Overview of Multi-Task Learning in Deep Neural Networks
Ruder, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05098 · Cited by 2,265
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393
One-Class Collaborative Filtering
Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.16 · Cited by 1,015
Learning and evaluating classifiers under sample selection bias
Zadrozny, B. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015425 · Cited by 800
Estimating conversion rate in display advertising from past erformance data
Lee, K.-C., Orten, B., Dasdan, A., & Li, W. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339651 · Cited by 197
Optimized Cost per Click in Taobao Display Advertising
Zhu, H., Jin, J., Tan, C., Pan, F., Zeng, Y., Li, H., & Gai, K. (2017) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098134 · Cited by 109
Self-citationBid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising
Zhang, W., Zhou, T., Wang, Y.-X., & Xu, J. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939713 · Cited by 55
Cited by
Bias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Tang, H., Liu, J., Zhao, M., & Gong, X. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3412236 · Cited by 631
What Your Next Check-in Might Look Like: Next Check-in Behavior Prediction
Sun, H., Cao, C., Chu, X., Hu, T., Lu, J., He, L., Wang, Z., He, H., & Xiong, H. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3625234 · Cited by 10
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Metadata
- Authors
- Ma, X., Zhao, L., Huang, G., Wang, Z., Hu, Z., Zhu, X., & Gai, K.
- Venue
- The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
- Publisher
- ACM
- Published
- 2018-06-27
- Pages
- 1137-1140
- OpenAlex
- W2962989965
- Cited by
- 382
- References
- 8
- FWCI
- 15.35
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Ma, X., Zhao, L., Huang, G., Wang, Z., Hu, Z., Zhu, X., & Gai, K. (2018). Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. https://doi.org/10.1145/3209978.3210104
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