Build a Recommendation System with the Multi-Armed Bandit Algorithm

Author(s): Flo

Originally published on Towards AI.

Data exploration, Data exploitation, and Continuous Learning

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stuffed animals-tisou, image by @walterwhites on OpenSea

The Multi-Armed Algorithm is a reinforcement learning algorithm used for resource allocation and decision-making. We will explain how the Multi-Armed technique helps to build a recommendation system for an online shop selling stuffed animals.

Concretely, the objective of this algorithm is to start by randomly trying different options (arms), collect data on the result and as it gathers data, it dynamically allocates more resources to the option with the best result.

We will dive into the details to explain how it works through 3 steps:

Data exploration: understand our data and products by experiment.Data exploitation: find… Read the full blog for free on Medium.

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Published via Towards AI