Thompson Sampling is a popular algorithm used to solve the multi-armed bandit problem. It is commonly employed in scenarios where an agent needs to make a sequence of decisions under uncertainty, such as in online advertising, clinical trials, and recommendation systems. The algorithm balances exploration and exploitation to maximize the total reward over time.
Technologies Used
- Python
- Chart.js
- FastAPI
About
This demo provide an efficient and interactive way to make personalized product recommendations based on user interactions. The Python backend calculates the top 5 recommended products using Thompson Sampling, and the HTML/JavaScript front-end displays these recommendations along with interactive charts. By allowing users to provide feedback, the algorithm adapts to users' preferences and provides more relevant recommendations over time. This combined implementation showcases the power of Thompson Sampling in solving real-world problems like personalized product recommendations.