Netflix Movie Recommendation using Multi-Armed Bandit Algorithm
In this post, we'll delve into the world of personalized movie recommendations using a Multi-Armed Bandit (MAB) algorithm, a popular choice in recommendation systems. Our goal is to develop an AI-powered movie recommendation engine that suggests the most enjoyable movies to users based on their past ratings.
Problem Statement
Given a dataset of user ratings for various movies, our task is to recommend the next movie that a user is likely to enjoy. The dataset consists of user IDs, movie IDs, ratings (1-5), and timestamps.
Multi-Armed Bandit Algorithm
The MAB algorithm, inspired by the problem of slot machines (one arm per machine), is an ideal choice for this task. It aims to optimize the choice of action (movie recommendation) at each time step, balancing exploration and exploitation.
Here's a simplified implementation in Python:
python
import numpy as np
from scipy.special import softmax
class MA...
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