Recommendation System Tutorial with Python using Collaborative Filtering

Original article was published by Towards AI Team on Artificial Intelligence on Medium

Calculate the average rating

def get_average_rating(sparse_matrix, is_user):
ax = 1 if is_user else 0
sum_of_ratings = sparse_matrix.sum(axis = ax).A1
no_of_ratings = (sparse_matrix != 0).sum(axis = ax).A1
rows, cols = sparse_matrix.shape
average_ratings = {i: sum_of_ratings[i]/no_of_ratings[i] for i in range(rows if is_user else cols) if no_of_ratings[i] != 0}
return average_ratings

Average Rating User

average_rating_user = get_average_rating(train_sparse_data, True)

Average Rating Movie

avg_rating_movie = get_average_rating(train_sparse_data, False)

Check Cold Start Problem: User

total_users = len(np.unique(netflix_rating_df["customer_id"]))
train_users = len(average_rating_user)
uncommonUsers = total_users - train_users

print("Total no. of Users = {}".format(total_users))
print("No. of Users in train data= {}".format(train_users))
print("No. of Users not present in train data = {}({}%)".format(uncommonUsers, np.round((uncommonUsers/total_users)*100), 2))
Figure 15: Cold start problem-user.

Here, 1% of total users are new, and they will have no proper rating available. Therefore, this can bring the issue of the cold start problem.

Check Cold Start Problem: Movie

total_movies = len(np.unique(netflix_rating_df["movie_id"]))
train_movies = len(avg_rating_movie)
uncommonMovies = total_movies - train_movies

print("Total no. of Movies = {}".format(total_movies))
print("No. of Movies in train data= {}".format(train_movies))
print("No. of Movies not present in train data = {}({}%)".format(uncommonMovies, np.round((uncommonMovies/total_movies)*100), 2))
Figure 16: Cold start problem-movie.

Here, 20% of total movies are new, and their rating might not be available in the dataset. Consequently, this can bring the issue of the cold start problem.

Similarity Matrix

A similarity matrix is critical to measure and calculate the similarity between user-profiles and movies to generate recommendations. Fundamentally, this kind of matrix calculates the similarity between two data points.

Figure 17: Similarity matrix.

In the matrix shown in figure 17, video2 and video5 are very similar. The computation of the similarity matrix is a very tedious job because it requires a powerful computational system.

Compute User Similarity Matrix

Computation of user similarity to find similarities of the top 100 users:

def compute_user_similarity(sparse_matrix, limit=100):
row_index, col_index = sparse_matrix.nonzero()
rows = np.unique(row_index)
similar_arr = np.zeros(61700).reshape(617,100)

for row in rows[:limit]:
sim = cosine_similarity(sparse_matrix.getrow(row), train_sparse_data).ravel()
similar_indices = sim.argsort()[-limit:]
similar = sim[similar_indices]
similar_arr[row] = similar

return similar_arrsimilar_user_matrix = compute_user_similarity(train_sparse_data, 100)

Compute Movie Similarity Matrix

Load movies title data set

movie_titles_df = pd.read_csv("movie_titles.csv",sep = ",", header = None, names=['movie_id', 'year_of_release', 'movie_title'],index_col = "movie_id", encoding = "iso8859_2")movie_titles_df.head()
Figure 18: Movie title list.

Compute similar movies:

def compute_movie_similarity_count(sparse_matrix, movie_titles_df, movie_id):
similarity = cosine_similarity(sparse_matrix.T, dense_output = False)
no_of_similar_movies = movie_titles_df.loc[movie_id][1], similarity[movie_id].count_nonzero()
return no_of_similar_movies

Get a similar movies list:

similar_movies = compute_movie_similarity_count(train_sparse_data, movie_titles_df, 1775)
print("Similar Movies = {}".format(similar_movies))
Figure 19: Similar movie list.

Building the Machine Learning Model

Create a Sample Sparse Matrix

def get_sample_sparse_matrix(sparseMatrix, n_users, n_movies):
users, movies, ratings = sparse.find(sparseMatrix)
uniq_users = np.unique(users)
uniq_movies = np.unique(movies)
userS = np.random.choice(uniq_users, n_users, replace = False)
movieS = np.random.choice(uniq_movies, n_movies, replace = False)
mask = np.logical_and(np.isin(users, userS), np.isin(movies, movieS))
sparse_sample = sparse.csr_matrix((ratings[mask], (users[mask], movies[mask])),
shape = (max(userS)+1, max(movieS)+1))
return sparse_sample

Sample Sparse Matrix for the training data:

train_sample_sparse_matrix = get_sample_sparse_matrix(train_sparse_data, 400, 40)

Sample Sparse Matrix for the test data:

test_sparse_matrix_matrix = get_sample_sparse_matrix(test_sparse_data, 200, 20)

Featuring the Data

Featuring is a process to create new features by adding different aspects of variables. Here, five similar profile users and similar types of movies features will be created. These new features help relate the similarities between different movies and users. Below new features will be added in the data set after featuring of data:

Figure 20: New features.
def create_new_similar_features(sample_sparse_matrix):
global_avg_rating = get_average_rating(sample_sparse_matrix, False)
global_avg_users = get_average_rating(sample_sparse_matrix, True)
global_avg_movies = get_average_rating(sample_sparse_matrix, False)
sample_train_users, sample_train_movies, sample_train_ratings = sparse.find(sample_sparse_matrix)
new_features_csv_file = open("/content/netflix_dataset/new_features.csv", mode = "w")

for user, movie, rating in zip(sample_train_users, sample_train_movies, sample_train_ratings):
similar_arr = list()

similar_users = cosine_similarity(sample_sparse_matrix[user], sample_sparse_matrix).ravel()
indices = np.argsort(-similar_users)[1:]
ratings = sample_sparse_matrix[indices, movie].toarray().ravel()
top_similar_user_ratings = list(ratings[ratings != 0][:5])
top_similar_user_ratings.extend([global_avg_rating[movie]] * (5 - len(ratings)))

similar_movies = cosine_similarity(sample_sparse_matrix[:,movie].T, sample_sparse_matrix.T).ravel()
similar_movies_indices = np.argsort(-similar_movies)[1:]
similar_movies_ratings = sample_sparse_matrix[user, similar_movies_indices].toarray().ravel()
top_similar_movie_ratings = list(similar_movies_ratings[similar_movies_ratings != 0][:5])
top_similar_movie_ratings.extend([global_avg_users[user]] * (5-len(top_similar_movie_ratings)))


new_features_csv_file.write(",".join(map(str, similar_arr)))

new_features_df = pd.read_csv('/content/netflix_dataset/new_features.csv', names = ["user_id", "movie_id", "gloabl_average", "similar_user_rating1",
"similar_user_rating2", "similar_user_rating3",
"similar_user_rating4", "similar_user_rating5",
"similar_movie_rating1", "similar_movie_rating2",
"similar_movie_rating3", "similar_movie_rating4",
"similar_movie_rating5", "user_average",
"movie_average", "rating"]) return new_features_df

Featuring (adding new similar features) for the training data:

train_new_similar_features = create_new_similar_features(train_sample_sparse_matrix)train_new_similar_features.head()
Figure 21: Featuring dataset for the training dataset.

Featuring (adding new similar features) for the test data:

test_new_similar_features = create_new_similar_features(test_sparse_matrix_matrix)test_new_similar_features.head()
Figure 22: Featuring dataset for the test dataset.

Training and Prediction of the Model

Divide the train and test data from the similar_features dataset:

x_train = train_new_similar_features.drop(["user_id", "movie_id", "rating"], axis = 1)x_test = test_new_similar_features.drop(["user_id", "movie_id", "rating"], axis = 1)y_train = train_new_similar_features["rating"]y_test = test_new_similar_features["rating"]

Utility method to check accuracy:

def error_metrics(y_true, y_pred):
rmse = np.sqrt(mean_squared_error(y_true, y_pred))
return rmse

Fit to XGBRegressor algorithm with 100 estimators:

clf = xgb.XGBRegressor(n_estimators = 100, silent = False, n_jobs  = 10), y_train)
Figure 23: XGB Regressor Algorithm

Predict the result of the test data set:

y_pred_test = clf.predict(x_test)

Check accuracy of predicted data:

rmse_test = error_metrics(y_test, y_pred_test)print("RMSE = {}".format(rmse_test))
Figure 24: Accuracy.

As shown in figure 24, the RMSE (Root mean squared error) for the predicted model dataset is 99%. If the accuracy is lower than our expectations, we would need to continue to train our model until the accuracy meets a high standard.

Plot Feature Importance

Feature importance is an important technique that selects a score to input features based on how valuable they are at predicting a target variable.

def plot_importance(model, clf):
fig = plt.figure(figsize = (8, 6))
ax = fig.add_axes([0,0,1,1])
model.plot_importance(clf, ax = ax, height = 0.3)
plt.xlabel("F Score", fontsize = 20)
plt.ylabel("Features", fontsize = 20)
plt.title("Feature Importance", fontsize = 20)
plt.tick_params(labelsize = 15), clf)
Figure 25: Feature importance plot.

The plot shown in figure 25 displays the feature importance of each feature. Here, the user_average rating is a critical feature. Its score is higher than the other features. Other features like similar user ratings and similar movie ratings have been created to relate the similarity between different users and movies.


Over the years, Machine learning has solved several challenges for companies like Netflix, Amazon, Google, Facebook, and others. The recommender system for Netflix helps the user filter through information in a massive list of movies and shows based on his/her choice. A recommender system must interact with the users to learn their preferences to provide recommendations.

Collaborative filtering (CF) is a very popular recommendation system algorithm for the prediction and recommendation based on other users’ ratings and collaboration. User-based collaborative filtering was the first automated collaborative filtering mechanism. It is also called k-NN collaborative filtering. The problem of collaborative filtering is to predict how well a user will like an item that he has not rated given a set of existing choice judgments for a population of users [4].