A recommender using matrix factorization in Python

from surprise import Dataset
from surprise import Reader
from surprise.model_selection import train_test_split
from surprise import SVD
from surprise import accuracy

# Load your data (replace this with your dataset)
reader = Reader(line_format='user item rating', sep=',')
data = Dataset.load_from_file('data.txt', reader=reader)

# Split the dataset into training and testing sets
trainset, testset = train_test_split(data, test_size=0.25)

# Use the SVD algorithm for matrix factorization
algo = SVD()
algo.fit(trainset)

# Make predictions on the test set
predictions = algo.test(testset)

# Evaluate the model's accuracy
accuracy.rmse(predictions)

# Replace 'user_id' and 'item_id' with the actual user and item IDs for which you want to get recommendations
user_id = 'user_id'
item_id = 'item_id'

# Get a prediction for a specific user and item
prediction = algo.predict(user_id, item_id)
print(f"Predicted rating for user {user_id} on item {item_id}: {prediction.est}")

The data.txt file should contain rows of data in the format specified by the Reader. In this case, the format is assumed to be “user item rating”, separated by commas.

user1, item1, 4.5
user1, item2, 3.0
user2, item1, 5.0
user2, item3, 2.5
user3, item2, 4.0
user3, item3, 3.5