Chapter 16: Personalization, Marketing, and Finance
In this chapter, we discuss three areas in which reinforcement learning is gaining significant traction. First, we describe how it can be used in personalization and recommendation systems. With that, we go beyond the single-step bandit approaches we covered in the earlier chapters. A related field that can also significantly benefit from reinforcement learning is marketing. In addition to personalized marketing applications, reinforcement learning can help in areas like managing campaign budgets and reducing customer churn. Finally, we discuss the promise of RL in finance and the related challenges. In doing so, we introduce TensorTrade, a Python framework for developing and testing RL-based trading algorithms.
So, in this chapter, we cover:
- Going beyond bandits for personalization,
- Developing effective marketing strategies using reinforcement learning,
- Applying reinforcement learning in finance.
