Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao

Author:

Gao Jingyue1ORCID,Han Shuguang1ORCID,Zhu Han1ORCID,Yang Siran1ORCID,Jiang Yuning1ORCID,Xu Jian1ORCID,Zheng Bo1ORCID

Affiliation:

1. Alibaba Group, Beijing, China

Publisher

ACM

Reference39 articles.

1. Rohan Anil , Sandra Gadanho , Da Huang , Nijith Jacob , Zhuoshu Li , Dong Lin , Todd Phillips , Cristina Pop , Kevin Regan , Gil I Shamir , 2022 . On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models. arXiv preprint arXiv:2209.05310 (2022). Rohan Anil, Sandra Gadanho, Da Huang, Nijith Jacob, Zhuoshu Li, Dong Lin, Todd Phillips, Cristina Pop, Kevin Regan, Gil I Shamir, et al. 2022. On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models. arXiv preprint arXiv:2209.05310 (2022).

2. Causal embeddings for recommendation

3. Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising;Bottou Léon;Journal of Machine Learning Research,2013

4. Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds

5. Brian Cheung , Jesse A Livezey , Arjun K Bansal , and Bruno A Olshausen . 2014. Discovering hidden factors of variation in deep networks. arXiv preprint arXiv:1412.6583 ( 2014 ). Brian Cheung, Jesse A Livezey, Arjun K Bansal, and Bruno A Olshausen. 2014. Discovering hidden factors of variation in deep networks. arXiv preprint arXiv:1412.6583 (2014).

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