Ilya Katsov
ABSTRACT
Traditional machine learning methods used in marketing and digital commerce applications, including propensity scoring and many recommendation algorithms, are usually focused on improving short-term outcomes such as click-through rates. In many environments, however, long-term customer engagement can be more important than immediate outcomes. In this paper, we describe several real-world case studies on building personalization engines that address this problem using reinforcement learning (RL) methods. We also discuss the design patterns used to create such solutions.
- Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Yuchen He, Zachary Kaden, Vivek Narayanan, Xiaohui Ye, Zhengxing Chen, and Scott Fujimoto. 2018. Horizon: Facebook's open source applied reinforcement learning platform. arXiv preprint arXiv:1811.00260 (2018).Google Scholar
- Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010. A contextualbandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web. 661--670.Google ScholarDigital Library
- Georgios Theocharous, Philip S Thomas, and Mohammad Ghavamzadeh. 2015. Personalized ad recommendation systems for life-time value optimization with guarantees. In Twenty-Fourth International Joint Conference on Artificial Intelligence.Google Scholar
Index Terms
- Building Next Best Action Engines for B2C and B2B Use Cases
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