item-item collaborative filtering

item-item collaborative filtering.py

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

from surprise import KNNWithMeans
from surprise import Dataset
from surprise import accuracy
from surprise.model_selection import train_test_split

# Load the movielens-100k dataset  UserID::MovieID::Rating::Timestamp
data = Dataset.load_builtin('ml-100k')
trainset, testset = train_test_split(data, test_size=.15)

# Use user_based true/false to switch between user-based or item-based collaborative filtering
algo = KNNWithMeans(k=50, sim_options={'name': 'pearson_baseline', 'user_based': False})
algo.fit(trainset)

# run the trained model against the testset
test_pred = algo.test(testset)

# get RMSE
print("Item-based Model : Test Set")
accuracy.rmse(test_pred, verbose=True)

# if you wanted to evaluate on the trainset
print("Item-based Model : Training Set")
train_pred = algo.test(trainset.build_testset())
accuracy.rmse(train_pred)