Reinforcement Learning (RL) is theoretically one of the most effective deep learning methods. However, in practice it doesn’t handle complex problems. RecSim, an RL framework developed at Google, allows the optimization of complex recommendation systems.
Researchers at Google and UT Austin created a more robust RL framework that allows for dynamic user features and handles some of the technical problems with RL. While the method is very computationally intensive, it provides an offline development environment that allows the optimization or recommendation engines.
Let’s dive in a see how the method works…
RecSim is a simulation environment builder that leverages reinforcement learning (RL). The method is controlled by a “simulator” module which is responsible for sampling users/documents and iteratively training a recommendation agent. The method supports sequential interactions with users and allows lots of customization of the environment by the engineer.
The paper and some other useful resources are linked in the comments.
Let’s slow down a bit and actually understand how the method works.
Background on Reinforcement Learning
Reinforcement Learning (RL) is a framework that involves training an agent to make decisions through repeated simulations. In short, the agent makes a decision, get’s feedback from the simulator, adjusts its decision, and tries again. This process is repeated until a loss function is optimized.
Modern RL algorithms were first applied to the Atari game, Breakout (figure 2). The agent controls the paddle at the bottom and looks to maximize score by breaking blocks with the ball. Simply through lots of attempts, RL can become a near perfect player. Since then, RL has received a lot of attention due to its ability to master well-defined tasks, such as games and NLP.
But, as with any machine learning algorithm, it has its limitations:
- RL doesn’t generalize well. If new features or decisions are introduced, it often struggles to adapt.
- RL doesn’t scale well on combinatorial decisions spaces. So, if we have lots of possible decisions, such as recommending lots of movies on Netflix’s home screen, RL struggles to handle the volume of possible configurations.
- RL doesn’t handle low signal-to-noise ratio data. RL is a very powerful model…
Continue reading: https://towardsdatascience.com/how-to-use-reinforcement-learning-to-recommend-content-6d7f9171b956?source=rss—-7f60cf5620c9—4