Last active
November 21, 2019 02:03
-
-
Save mahermalaeb/3d03feb1bbada7e7e1438f86b1a8abb9 to your computer and use it in GitHub Desktop.
The easy guide for building python collaborative filtering recommendation system in 2017
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import zipfile | |
from surprise import Reader, Dataset, SVD, evaluate | |
# Unzip ml-100k.zip | |
zipfile = zipfile.ZipFile('ml-100k.zip', 'r') | |
zipfile.extractall() | |
zipfile.close() | |
# Read data into an array of strings | |
with open('./ml-100k/u.data') as f: | |
all_lines = f.readlines() | |
# Prepare the data to be used in Surprise | |
reader = Reader(line_format='user item rating timestamp', sep='\t') | |
data = Dataset.load_from_file('./ml-100k/u.data', reader=reader) | |
# Split the dataset into 5 folds and choose the algorithm | |
data.split(n_folds=5) | |
algo = SVD() | |
# Train and test reporting the RMSE and MAE scores | |
evaluate(algo, data, measures=['RMSE', 'MAE']) | |
# Retrieve the trainset. | |
trainset = data.build_full_trainset() | |
algo.train(trainset) | |
# Predict a certain item | |
userid = str(196) | |
itemid = str(302) | |
actual_rating = 4 | |
print(algo.predict(userid, itemid, actual_rating)) |
Hello,
I'm a beginner and I use Python 3.7. I receive the following feedback when using your code:
ModuleNotFoundError: No module named 'surprise'
Could someone help me please?
Hi,
that just means you haven't installed surprise yet. In your python shell run "pip install scikit-surprise" or in your conda environment "conda install -c conda-forge scikit-surprise".
If you don't know what any of that means, I'd suggest starting at the beginning (with a python course or something) and not with recommender systems ;)
Hi. Now i want to change back the data type back to DataFrame (pandas) after applying SVD. Could you suggest the way to do it. Thanks
How to get the latent feature (or embedding) for each user and item from the trained model?
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
There is no native function to predict top items for a specific user. However you can check this FAQ part of the documentation of Surprise How to get the top-N recommendations for each user. This example shows how to predict top-n items for all users but you can modify the code to predict items for a specific user.