Budget Contextual Bandits

Constrained Contextual Bandits for Personalized Recommendation. Extending a few popular MAB/CB methods by adding budget constraints.

• Context-Free Epsilon Greedy
• LinUCB [1]
• UCB-ALP [2]
• HATCH [3]

Get started

Now you can simply install it from PyPI:

pip install budget-constrained-CB

Benchmark different methods against random and greedy policy

Check out the notebook to see the comparison among a variety of methods including UCB-ALP, LinUCB and HATCH, against the two baseline policies Random and Greedy.

Rolling mean of rewards v.s. number of rounds:

Reference

[1] Li, Lihong, et al. "A contextual-bandit approach to personalized news article recommendation." Proceedings of the 19th international conference on World wide web. 2010.

[2] Wu, Huasen, et al. "Algorithms with logarithmic or sublinear regret for constrained contextual bandits." Advances in Neural Information Processing Systems. 2015.

[3] Yang, Mengyue, et al. "Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation." Proceedings of The Web Conference 2020. 2020.