How to do an item-item Similarity recommendation?

import numpy as np

from sklearn.metrics.pairwise import cosine_similarity

# Sample ratings data (user-item matrix)

ratings = np.array([

    [5, 3, 4, 4, 0],  # User 1

    [1, 0, 5, 0, 4],  # User 2

    [0, 3, 0, 4, 0],  # User 3

    [5, 0, 4, 3, 5]   # User 4

])

# Calculate item-item similarity matrix using cosine similarity

item_similarity = cosine_similarity(ratings.T)

# Function to generate item recommendations for a given item

def get_item_recommendations(item_id, top_n=3):

    item_scores = item_similarity[item_id]

    top_items = np.argsort(item_scores)[-top_n-1:-1][::-1]

    return top_items

# Example usage:

item_id = 2  # Item ID for which recommendations are needed

recommendations = get_item_recommendations(item_id, top_n=3)

print(f"Top recommendations for Item {item_id}: {recommendations}")