A Practical Online Allocation Framework at Industry-scale in Constrained Recommendation

Author:

Jian Daohong1ORCID,Bao Yang1ORCID,Zhou Jun1ORCID,Wu Hua1ORCID

Affiliation:

1. AntGroup, Beijing, China

Publisher

ACM

Reference32 articles.

1. Shipra Agrawal , Morteza Zadimoghaddam , and Vahab Mirrokni . 2018 . Propor- tional allocation: Simple, distributed, and diverse matching with high entropy . In International Conference on Machine Learning. PMLR, 99--108 . Shipra Agrawal, Morteza Zadimoghaddam, and Vahab Mirrokni. 2018. Propor- tional allocation: Simple, distributed, and diverse matching with high entropy. In International Conference on Machine Learning. PMLR, 99--108.

2. Mohammad Ali Alomrani , Reza Moravej , and Elias B Khalil . 2021. Deep policies for online bipartite matching: a reinforcement learning approach. arXiv preprint arXiv:2109.10380 ( 2021 ). Mohammad Ali Alomrani, Reza Moravej, and Elias B Khalil. 2021. Deep policies for online bipartite matching: a reinforcement learning approach. arXiv preprint arXiv:2109.10380 (2021).

3. Stephen Boyd , Stephen P Boyd , and Lieven Vandenberghe . 2004. Convex optimization . Cambridge university press . Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe. 2004. Convex optimization. Cambridge university press.

4. Stephen Boyd Neal Parikh Eric Chu Borja Peleato Jonathan Eckstein etal 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning 3 1 (2011) 1--122. Stephen Boyd Neal Parikh Eric Chu Borja Peleato Jonathan Eckstein et al. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning 3 1 (2011) 1--122.

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