CONTENT-BASED FILTERING

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous, and can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library.

Acquiring the Data

To acquire and extract the data, simply run the following Bash scripts:
Dataset acquired from GroupLens. Lets download the dataset. To download the data, we will use !wget to download it from IBM Object Storage.
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In [1]:
!wget -O moviedataset.zip https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/moviedataset.zip
print('unziping ...')
!unzip -o -j moviedataset.zip 
--2019-05-06 02:27:18--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/moviedataset.zip
Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193
Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 160301210 (153M) [application/zip]
Saving to: ‘moviedataset.zip’

moviedataset.zip    100%[===================>] 152.88M  33.1MB/s    in 4.6s    

2019-05-06 02:27:23 (33.1 MB/s) - ‘moviedataset.zip’ saved [160301210/160301210]

unziping ...
Archive:  moviedataset.zip
  inflating: links.csv               
  inflating: movies.csv              
  inflating: ratings.csv             
  inflating: README.txt              
  inflating: tags.csv                

Now you're ready to start working with the data!

Preprocessing

First, let's get all of the imports out of the way:

In [2]:
#Dataframe manipulation library
import pandas as pd
#Math functions, we'll only need the sqrt function so let's import only that
from math import sqrt
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

Now let's read each file into their Dataframes:

In [3]:
#Storing the movie information into a pandas dataframe
movies_df = pd.read_csv('movies.csv')
#Storing the user information into a pandas dataframe
ratings_df = pd.read_csv('ratings.csv')
#Head is a function that gets the first N rows of a dataframe. N's default is 5.
movies_df.head()
Out[3]:
movieId title genres
0 1 Toy Story (1995) Adventure|Animation|Children|Comedy|Fantasy
1 2 Jumanji (1995) Adventure|Children|Fantasy
2 3 Grumpier Old Men (1995) Comedy|Romance
3 4 Waiting to Exhale (1995) Comedy|Drama|Romance
4 5 Father of the Bride Part II (1995) Comedy

Let's also remove the year from the title column by using pandas' replace function and store in a new year column.

In [4]:
#Using regular expressions to find a year stored between parentheses
#We specify the parantheses so we don't conflict with movies that have years in their titles
movies_df['year'] = movies_df.title.str.extract('(\(\d\d\d\d\))',expand=False)
#Removing the parentheses
movies_df['year'] = movies_df.year.str.extract('(\d\d\d\d)',expand=False)
#Removing the years from the 'title' column
movies_df['title'] = movies_df.title.str.replace('(\(\d\d\d\d\))', '')
#Applying the strip function to get rid of any ending whitespace characters that may have appeared
movies_df['title'] = movies_df['title'].apply(lambda x: x.strip())
movies_df.head()
Out[4]:
movieId title genres year
0 1 Toy Story Adventure|Animation|Children|Comedy|Fantasy 1995
1 2 Jumanji Adventure|Children|Fantasy 1995
2 3 Grumpier Old Men Comedy|Romance 1995
3 4 Waiting to Exhale Comedy|Drama|Romance 1995
4 5 Father of the Bride Part II Comedy 1995

With that, let's also split the values in the Genres column into a list of Genres to simplify future use. This can be achieved by applying Python's split string function on the correct column.

In [5]:
#Every genre is separated by a | so we simply have to call the split function on |
movies_df['genres'] = movies_df.genres.str.split('|')
movies_df.head()
Out[5]:
movieId title genres year
0 1 Toy Story [Adventure, Animation, Children, Comedy, Fantasy] 1995
1 2 Jumanji [Adventure, Children, Fantasy] 1995
2 3 Grumpier Old Men [Comedy, Romance] 1995
3 4 Waiting to Exhale [Comedy, Drama, Romance] 1995
4 5 Father of the Bride Part II [Comedy] 1995

Since keeping genres in a list format isn't optimal for the content-based recommendation system technique, we will use the One Hot Encoding technique to convert the list of genres to a vector where each column corresponds to one possible value of the feature. This encoding is needed for feeding categorical data. In this case, we store every different genre in columns that contain either 1 or 0. 1 shows that a movie has that genre and 0 shows that it doesn't. Let's also store this dataframe in another variable since genres won't be important for our first recommendation system.

In [6]:
#Copying the movie dataframe into a new one since we won't need to use the genre information in our first case.
moviesWithGenres_df = movies_df.copy()

#For every row in the dataframe, iterate through the list of genres and place a 1 into the corresponding column
for index, row in movies_df.iterrows():
    for genre in row['genres']:
        moviesWithGenres_df.at[index, genre] = 1
#Filling in the NaN values with 0 to show that a movie doesn't have that column's genre
moviesWithGenres_df = moviesWithGenres_df.fillna(0)
moviesWithGenres_df.head()
Out[6]:
movieId title genres year Adventure Animation Children Comedy Fantasy Romance ... Horror Mystery Sci-Fi IMAX Documentary War Musical Western Film-Noir (no genres listed)
0 1 Toy Story [Adventure, Animation, Children, Comedy, Fantasy] 1995 1.0 1.0 1.0 1.0 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 2 Jumanji [Adventure, Children, Fantasy] 1995 1.0 0.0 1.0 0.0 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 3 Grumpier Old Men [Comedy, Romance] 1995 0.0 0.0 0.0 1.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 4 Waiting to Exhale [Comedy, Drama, Romance] 1995 0.0 0.0 0.0 1.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 5 Father of the Bride Part II [Comedy] 1995 0.0 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 24 columns

Next, let's look at the ratings dataframe.

In [7]:
ratings_df.head()
Out[7]:
userId movieId rating timestamp
0 1 169 2.5 1204927694
1 1 2471 3.0 1204927438
2 1 48516 5.0 1204927435
3 2 2571 3.5 1436165433
4 2 109487 4.0 1436165496

Every row in the ratings dataframe has a user id associated with at least one movie, a rating and a timestamp showing when they reviewed it. We won't be needing the timestamp column, so let's drop it to save on memory.

In [8]:
#Drop removes a specified row or column from a dataframe
ratings_df = ratings_df.drop('timestamp', 1)
ratings_df.head()
Out[8]:
userId movieId rating
0 1 169 2.5
1 1 2471 3.0
2 1 48516 5.0
3 2 2571 3.5
4 2 109487 4.0

Content-Based recommendation system

Now, let's take a look at how to implement Content-Based or Item-Item recommendation systems. This technique attempts to figure out what a user's favourite aspects of an item is, and then recommends items that present those aspects. In our case, we're going to try to figure out the input's favorite genres from the movies and ratings given.

Let's begin by creating an input user to recommend movies to:

Notice: To add more movies, simply increase the amount of elements in the userInput. Feel free to add more in! Just be sure to write it in with capital letters and if a movie starts with a "The", like "The Matrix" then write it in like this: 'Matrix, The' .

In [9]:
userInput = [
            {'title':'Breakfast Club, The', 'rating':5},
            {'title':'Toy Story', 'rating':3.5},
            {'title':'Jumanji', 'rating':2},
            {'title':"Pulp Fiction", 'rating':5},
            {'title':'Akira', 'rating':4.5}
         ] 
inputMovies = pd.DataFrame(userInput)
inputMovies
Out[9]:
rating title
0 5.0 Breakfast Club, The
1 3.5 Toy Story
2 2.0 Jumanji
3 5.0 Pulp Fiction
4 4.5 Akira

Add movieId to input user

With the input complete, let's extract the input movie's ID's from the movies dataframe and add them into it.

We can achieve this by first filtering out the rows that contain the input movie's title and then merging this subset with the input dataframe. We also drop unnecessary columns for the input to save memory space.

In [11]:
#Filtering out the movies by title
inputId = movies_df[movies_df['title'].isin(inputMovies['title'].tolist())]
#Then merging it so we can get the movieId. It's implicitly merging it by title.
inputMovies = pd.merge(inputId, inputMovies)
#Dropping information we won't use from the input dataframe
inputMovies = inputMovies.drop('genres', 1).drop('year', 1)
#Final input dataframe
#If a movie you added in above isn't here, then it might not be in the original 
#dataframe or it might spelled differently, please check capitalisation.
inputMovies
Out[11]:
movieId title rating
0 1 Toy Story 3.5
1 2 Jumanji 2.0
2 296 Pulp Fiction 5.0
3 1274 Akira 4.5
4 1968 Breakfast Club, The 5.0

We're going to start by learning the input's preferences, so let's get the subset of movies that the input has watched from the Dataframe containing genres defined with binary values.

In [12]:
#Filtering out the movies from the input
userMovies = moviesWithGenres_df[moviesWithGenres_df['movieId'].isin(inputMovies['movieId'].tolist())]
userMovies
Out[12]:
movieId title genres year Adventure Animation Children Comedy Fantasy Romance ... Horror Mystery Sci-Fi IMAX Documentary War Musical Western Film-Noir (no genres listed)
0 1 Toy Story [Adventure, Animation, Children, Comedy, Fantasy] 1995 1.0 1.0 1.0 1.0 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 2 Jumanji [Adventure, Children, Fantasy] 1995 1.0 0.0 1.0 0.0 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
293 296 Pulp Fiction [Comedy, Crime, Drama, Thriller] 1994 0.0 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1246 1274 Akira [Action, Adventure, Animation, Sci-Fi] 1988 1.0 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1885 1968 Breakfast Club, The [Comedy, Drama] 1985 0.0 0.0 0.0 1.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 24 columns

We'll only need the actual genre table, so let's clean this up a bit by resetting the index and dropping the movieId, title, genres and year columns.

In [13]:
#Resetting the index to avoid future issues
userMovies = userMovies.reset_index(drop=True)
#Dropping unnecessary issues due to save memory and to avoid issues
userGenreTable = userMovies.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)
userGenreTable
Out[13]:
Adventure Animation Children Comedy Fantasy Romance Drama Action Crime Thriller Horror Mystery Sci-Fi IMAX Documentary War Musical Western Film-Noir (no genres listed)
0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 1.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Now we're ready to start learning the input's preferences!

To do this, we're going to turn each genre into weights. We can do this by using the input's reviews and multiplying them into the input's genre table and then summing up the resulting table by column. This operation is actually a dot product between a matrix and a vector, so we can simply accomplish by calling Pandas's "dot" function.

In [14]:
inputMovies['rating']
Out[14]:
0    3.5
1    2.0
2    5.0
3    4.5
4    5.0
Name: rating, dtype: float64
In [18]:
#Dot produt to get weights
userProfile = userGenreTable.transpose().dot(inputMovies['rating'])
#The user profile
userProfile
Out[18]:
Adventure             10.0
Animation              8.0
Children               5.5
Comedy                13.5
Fantasy                5.5
Romance                0.0
Drama                 10.0
Action                 4.5
Crime                  5.0
Thriller               5.0
Horror                 0.0
Mystery                0.0
Sci-Fi                 4.5
IMAX                   0.0
Documentary            0.0
War                    0.0
Musical                0.0
Western                0.0
Film-Noir              0.0
(no genres listed)     0.0
dtype: float64

Now, we have the weights for every of the user's preferences. This is known as the User Profile. Using this, we can recommend movies that satisfy the user's preferences.

Let's start by extracting the genre table from the original dataframe:

In [19]:
#Now let's get the genres of every movie in our original dataframe
genreTable = moviesWithGenres_df.set_index(moviesWithGenres_df['movieId'])
#And drop the unnecessary information
genreTable = genreTable.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)
genreTable.head()
Out[19]:
Adventure Animation Children Comedy Fantasy Romance Drama Action Crime Thriller Horror Mystery Sci-Fi IMAX Documentary War Musical Western Film-Noir (no genres listed)
movieId
1 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
5 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
In [16]:
genreTable.shape
Out[16]:
(34208, 20)

With the input's profile and the complete list of movies and their genres in hand, we're going to take the weighted average of every movie based on the input profile and recommend the top twenty movies that most satisfy it.

In [20]:
#Multiply the genres by the weights and then take the weighted average
recommendationTable_df = ((genreTable*userProfile).sum(axis=1))/(userProfile.sum())
recommendationTable_df.head()
Out[20]:
movieId
1    0.594406
2    0.293706
3    0.188811
4    0.328671
5    0.188811
dtype: float64
In [21]:
#Sort our recommendations in descending order
recommendationTable_df = recommendationTable_df.sort_values(ascending=False)
#Just a peek at the values
recommendationTable_df.head()
Out[21]:
movieId
5018      0.748252
26093     0.734266
27344     0.720280
148775    0.685315
6902      0.678322
dtype: float64

Now here's the recommendation table!

In [22]:
#The final recommendation table
movies_df.loc[movies_df['movieId'].isin(recommendationTable_df.head(20).keys())]
Out[22]:
movieId title genres year
664 673 Space Jam [Adventure, Animation, Children, Comedy, Fanta... 1996
1824 1907 Mulan [Adventure, Animation, Children, Comedy, Drama... 1998
2902 2987 Who Framed Roger Rabbit? [Adventure, Animation, Children, Comedy, Crime... 1988
4923 5018 Motorama [Adventure, Comedy, Crime, Drama, Fantasy, Mys... 1991
6793 6902 Interstate 60 [Adventure, Comedy, Drama, Fantasy, Mystery, S... 2002
8605 26093 Wonderful World of the Brothers Grimm, The [Adventure, Animation, Children, Comedy, Drama... 1962
8783 26340 Twelve Tasks of Asterix, The (Les douze travau... [Action, Adventure, Animation, Children, Comed... 1976
9296 27344 Revolutionary Girl Utena: Adolescence of Utena... [Action, Adventure, Animation, Comedy, Drama, ... 1999
9825 32031 Robots [Adventure, Animation, Children, Comedy, Fanta... 2005
11716 51632 Atlantis: Milo's Return [Action, Adventure, Animation, Children, Comed... 2003
11751 51939 TMNT (Teenage Mutant Ninja Turtles) [Action, Adventure, Animation, Children, Comed... 2007
13250 64645 The Wrecking Crew [Action, Adventure, Comedy, Crime, Drama, Thri... 1968
16055 81132 Rubber [Action, Adventure, Comedy, Crime, Drama, Film... 2010
18312 91335 Gruffalo, The [Adventure, Animation, Children, Comedy, Drama] 2009
22778 108540 Ernest & Célestine (Ernest et Célestine) [Adventure, Animation, Children, Comedy, Drama... 2012
22881 108932 The Lego Movie [Action, Adventure, Animation, Children, Comed... 2014
25218 117646 Dragonheart 2: A New Beginning [Action, Adventure, Comedy, Drama, Fantasy, Th... 2000
26442 122787 The 39 Steps [Action, Adventure, Comedy, Crime, Drama, Thri... 1959
32854 146305 Princes and Princesses [Animation, Children, Comedy, Drama, Fantasy, ... 2000
33509 148775 Wizards of Waverly Place: The Movie [Adventure, Children, Comedy, Drama, Fantasy, ... 2009

Advantages and Disadvantages of Content-Based Filtering

Advantages
  • Learns user's preferences
  • Highly personalized for the user
Disadvantages
  • Doesn't take into account what others think of the item, so low quality item recommendations might happen
  • Extracting data is not always intuitive
  • Determining what characteristics of the item the user dislikes or likes is not always obvious

Want to learn more?

IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: SPSS Modeler

Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at Watson Studio

Thanks for completing this lesson!

Author: Saeed Aghabozorgi

Saeed Aghabozorgi, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.


Copyright © 2018 Cognitive Class. This notebook and its source code are released under the terms of the MIT License.