Next best action how to#

First, RL-based recommenders can consider this sequential nature in contrast with the more traditional methods (such as collaborative filtering). The advantages that reinforcement learning (RL) based recommenders have over traditional recommenders are twofold. Figure 1: an agent interacting with the environment The agent repeats the interaction many times and learns what action is optimal at each state. Then, it receives a reward in correspondence with the action and the state it transitioned to. This agent chooses an action and observes what happens in the environment after it took the action. In a MDP, there is an agent interacting with an environment. Why reinforcement learning?Īs the nature of a next best action recommendation problem is sequential, this means that the problem can be modeled as a Markov Decision Process (MDP) and solved by reinforcement learning methods. In this blog post, we will go into more details as to why reinforcement learning could help us and how we actually implemented this. That is the reason why we introduced reinforcement learning as our lifesaver. A quick recapĪs mentioned in the initial post of this series, the baseline logistic regression model had many drawbacks when recommending a next best action: it could not incorporate the future, could not leverage the sequential nature of the data, could not recommend an optimal sequence of actions nor could it adjust automatically to changing environment dynamics. In this article, we try to demystify and explain how we used offline reinforcement learning to have a good baseline model for optimizing marketing campaigns.

Next best action how to#

You have heard about reinforcement learning for next best action optimization but don't really know why you would use it over other techniques or how to use it best?