Netflix because you watched feature Where are we at? This is what we did so far In , we downloaded our data from , did some EDA and created our user item matrix. The matrix has 671 unique users, 9066 unique movies and is 98.35% sparse part 0 MovieLens In , we described 3 of the most common recommendation methods: User Based Collaborative Filtering, Item Based Collaborative Filtering and Matrix Factorization part 1 In , we implemented Matrix Factorization through ALS and found similar movies part 2 In part 3, this part, we recommend movies to users based on what movies they’ve rated. We also make an attempt to clone Netflix’s “because you watched X” feature and make a complete page recommendation with trending movies Recommending Movies to users We pick up our code where we trained the ALS model from library. Previous code to load and process the data can be found in the previous posts in this series or on my . implicit Github model = implicit.als.AlternatingLeastSquares(factors=10,iterations=20,regularization=0.1,num_threads=4) model.fit(user_item.T) First let’s write a function that returns the movies that a particular user had rated def get_rated_movies_ids(user_id, user_item, users, movies):“””Input user_id: intUser ID user_item: scipy.Sparse MatrixUser item interaction matrix users: np.arrayMapping array between user ID and index in the user item matrix movies: np.arrayMapping array between movie ID and index in the user item matrix Output movieTableIDs: python listList of movie IDs that the user had rated “””user_id = users.index(user_id) Get matrix ids of rated movies by selected user ids = user_item[user_id].nonzero()[1] Convert matrix ids to movies IDs movieTableIDs = [movies[item] for item in ids] return movieTableIDs movieTableIDs = get_rated_movies_ids(1, user_item, users, movies)rated_movies = pd.DataFrame(movieTableIDs, columns=[‘movieId’])rated_movies def get_movies(movieTableIDs, movies_table):“””Input movieTableIDs: python listList of movie IDs that the user had rated movies_table: pd.DataFrameDataFrame of movies info Output rated_movies: pd.DataFrameDataFrame of rated movies “”” rated_movies = pd.DataFrame(movieTableIDs, columns=[‘movieId’]) rated_movies = pd.merge(rated_movies, movies_table, on=’movieId’, how=’left’) return rated_movies movieTableIDs = get_rated_movies_ids(1, user_item, users, movies)df = get_movies(movieTableIDs, movies_table)df Now, let’s recommend movieIDs for a particular user ID based on the movies that they rated. def recommend_movie_ids(user_id, model, user_item, users, movies, N=5):“””Input user_id: intUser ID model: ALS modelTrained ALS model user_item: sp.Sparse MatrixUser item interaction matrix so that we do not recommend already rated movies users: np.arrayMapping array between User ID and user item index movies: np.arrayMapping array between Movie ID and user item index N: int (default =5)Number of recommendations Output movies_ids: python listList of movie IDs“”” user_id = users.index(user_id) recommendations = model.recommend(user_id, user_item, N=N) recommendations = [item[0] for item in recommendations] movies_ids = [movies[ids] for ids in recommendations] return movies_ids movies_ids = recommend_movie_ids(1, model, user_item, users, movies, N=5)movies_ids > [1374, 1127, 1214, 1356, 1376] movies_rec = get_movies(movies_ids, movies_table)movies_rec display_posters(movies_rec) movies_ids = recommend_movie_ids(100, model, user_item, users, movies, N=7)movies_rec = get_movies(movies_ids, movies_table)display_posters(movies_rec) Because You watched Let’s implement Netflix “Because You Watched” feature. It’s about recommending movies based on what you’ve watched. This is similar to what we already did, but this time, it’s more selective. Here’s how we will do it: We will choose random 5 movies that a user had watched and for each movie recommend similar movies to it. Finally, we display all of them in a one page layout def similar_items(item_id, movies_table, movies, N=5):“””Input-----item_id: intMovieID in the movies table movies\_table: DataFrame DataFrame with movie ids, movie title and genre movies: np.array Mapping between movieID in the movies\_table and id in the item user matrix N: int Number of similar movies to return Output ----- df: DataFrame DataFrame with selected movie in first row and similar movies for N next rows “”” # Get movie user index from the mapping array user\_item\_id = movies.index(item\_id) # Get similar movies from the ALS model similars = model.similar\_items(user\_item\_id, N=N+1) # ALS similar\_items provides (id, score), we extract a list of ids l = \[item\[0\] for item in similars\[1:\]\] # Convert those ids to movieID from the mapping array ids = \[movies\[ids\] for ids in l\] # Make a dataFrame of the movieIds ids = pd.DataFrame(ids, columns=\[‘movieId’\]) # Add movie title and genres by joining with the movies table recommendation = pd.merge(ids, movies\_table, on=’movieId’, how=’left’) return recommendation def similar_and_display(item_id, movies_table, movies, N=5): df = similar\_items(item\_id, movies\_table, movies, N=N) df.dropna(inplace=True) display\_posters(df) def because_you_watched(user, user_item, users, movies, k=5, N=5):“””Input-----user: intUser ID user\_item: scipy sparse matrix User item interaction matrix users: np.array Mapping array between User ID and user item index movies: np.array Mapping array between Movie ID and user item index k: int Number of recommendations per movie N: int Number of movies already watched chosen “”” movieTableIDs = get\_rated\_movies\_ids(user, user\_item, users, movies) df = get\_movies(movieTableIDs, movies\_table) movieIDs = random.sample(df.movieId, N) for movieID in movieIDs: title = df\[df.movieId == movieID\].iloc\[0\].title print(“Because you’ve watched “, title) similar\_and\_display(movieID, movies\_table, movies, k) because_you_watched(500, user_item, users, movies, k=5, N=5) “Because you watched “, ‘Definitely, Maybe (2008)’ ’ “Because you watched “, ‘Pocahontas (1995) “Because you watched “, ‘Simpsons Movie, The (2007)’ “Because you watched “, ‘Catch Me If You Can (2002)’ “Because you watched “, ‘Risky Business (1983)’ Trending movies Let’s also implement trending movies. In our context, trending movies are movies that been rated the most by users def get_trending(user_item, movies, movies_table, N=5):“””Input user_item: scipy sparse matrixUser item interaction matrix to use to extract popular movies movies: np.arrayMapping array between movieId and ID in the user_item matrix movies_table: pd.DataFrameDataFrame for movies information N: intTop N most popular movies to return “”” binary = user_item.copy()binary[binary !=0] = 1 populars = np.array(binary.sum(axis=0)).reshape(-1) movieIDs = populars.argsort()[::-1][:N] movies_rec = get_movies(movieIDs, movies_table) movies_rec.dropna(inplace=True) print(“Trending Now”) display_posters(movies_rec) get_trending(user_item, movies, movies_table, N=6) Trending Now Page recommendation Let’s put everything in a timeline method. The timeline method will get the user ID and display trending movies and recommendations based on similar movies that that user had watched. def my_timeline(user, user_item, users, movies, movies_table, k=5, N=5): get\_trending(user\_item, movies, movies\_table, N=N) because\_you\_watched(user, user\_item, users, movies, k=k, N=N) my_timeline(500, user_item, users, movies, movies_table, k=5, N=5) Trending Now “Because you watched “, ‘Definitely, Maybe (2008)’ “Because you watched “, ‘Pocahontas (1995)’ “Because you watched “, ‘Simpsons Movie, The (2007)’ “Because you watched “, ‘Catch Me If You Can (2002)’ “Because you watched “, ‘Risky Business (1983)’ Export trained models to be used in production At this point, we want to get our model into production. We want to create a web service where a user will provide a userid to the service and the service will return all of the recommendations including the trending and the “because you’ve watched”. To do that, We first export the trained model and the used data for use in the web service. import scipy.sparse scipy.sparse.save_npz(‘model/user_item.npz’, user_item) np.save(‘model/movies.npy’, movies)np.save(‘model/users.npy’, users)movies_table.to_csv(‘model/movies_table.csv’, index=False) from sklearn.externals import joblibjoblib.dump(model, ‘model/model.pkl’) Conclusion In this post, we recommend movies to users based on their movie rating history. From there, we tried to clone the “because you watched” feature from Netflix and also display Trending movies as movies that were rated the most number of times. In the next post, we will try to put our work in a web service, where a user requests movie recommendations by providing its user ID. Stay tuned!
