M Hendra Herviawan

Movie Recommendation based on K-Nearest-Neighbors (KNN)

KNN is a simple concept: define some distance metric between the items in your dataset, and find the K closest items. You can then use those items to predict some property of a test item, by having them somehow "vote" on it.

Example of k-NN classification (From Wikipedia): The test sample (green circle) should be classified either to the first class of blue squares or to the second class of red triangles. If k = 3 (solid line circle) it is assigned to the second class because there are 2 triangles and only 1 square inside the inner circle. If k = 5 (dashed line circle) it is assigned to the first class (3 squares vs. 2 triangles inside the outer circle).

As an example, let's look at the MovieLens data. We'll try to guess the rating of a movie by looking at the 10 movies that are closest to it in terms of genres and popularity.

import pandas as pd
import numpy as np

r_cols = ['user_id', 'movie_id', 'rating']
ratings = pd.read_csv('ml-100k/u.data', sep='\t', names=r_cols, usecols=range(3))
ratings.head()
user_id movie_id rating
0 0 50 5
1 0 172 5
2 0 133 1
3 196 242 3
4 186 302 3

Now, we'll group everything by movie ID, and compute the total number of ratings (each movie's popularity) and the average rating for every movie:

movieProperties = ratings.groupby('movie_id').agg({'rating': [np.size, np.mean]})
movieProperties.head()
rating
size mean
movie_id
1 452 3.878319
2 131 3.206107
3 90 3.033333
4 209 3.550239
5 86 3.302326

The raw number of ratings isn't very useful for computing distances between movies, so we'll create a new DataFrame that contains the normalized number of ratings. So, a value of 0 means nobody rated it, and a value of 1 will mean it's the most popular movie there is.

movieNumRatings = pd.DataFrame(movieProperties['rating']['size'])
movieNormalizedNumRatings = movieNumRatings.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))
movieNormalizedNumRatings.head()
size
movie_id
1 0.773585
2 0.222985
3 0.152659
4 0.356775
5 0.145798

Now, let's get the genre information from the u.item file. The way this works is there are 19 fields, each corresponding to a specific genre - a value of '0' means it is not in that genre, and '1' means it is in that genre. A movie may have more than one genre associated with it.

While we're at it, we'll put together everything into one big Python dictionary called movieDict. Each entry will contain the movie name, list of genre values, the normalized popularity score, and the average rating for each movie:

movieDict = {}
with open(r'ml-100k/u.item', encoding="ISO-8859-1") as f:
    temp = ''
    for line in f:
        #line.encode().decode("ISO-8859-1")
        fields = line.rstrip('\n').split('|')
        movieID = int(fields[0])
        name = fields[1]
        genres = fields[5:25]
        genres = map(int, genres)
        movieDict[movieID] = (name, np.array(list(genres)), movieNormalizedNumRatings.loc[movieID].get('size'), movieProperties.loc[movieID].rating.get('mean'))

For example, here's the record we end up with for movie ID 1, "Toy Story":

print(movieDict[1])

('Toy Story (1995)', array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 0.77358490566037741, 3.8783185840707963)

Now let's define a function that computes the "distance" between two movies based on how similar their genres are, and how similar their popularity is. Just to make sure it works, we'll compute the distance between movie ID's 2 and 4:

from scipy import spatial

def ComputeDistance(a, b):
    genresA = a[1]
    genresB = b[1]
    genreDistance = spatial.distance.cosine(genresA, genresB)
    popularityA = a[2]
    popularityB = b[2]
    popularityDistance = abs(popularityA - popularityB)
    return genreDistance + popularityDistance

ComputeDistance(movieDict[1], movieDict[4])

1.0834762721555173

Remember the higher the distance, the less similar the movies are. Let's check what movies 2 and 4 actually are - and confirm they're not really all that similar:

Now, we just need a little code to compute the distance between some given test movie (Toy Story, in this example) and all of the movies in our data set. When the sort those by distance, and print out the K nearest neighbors:

import operator

def getNeighbors(movieID, K):
    distances = []
    for movie in movieDict:
        if (movie != movieID):
            dist = ComputeDistance(movieDict[movieID], movieDict[movie])
            distances.append((movie, dist))
    distances.sort(key=operator.itemgetter(1))
    neighbors = []
    for x in range(K):
        neighbors.append(distances[x][0])
    return neighbors

K = 10
avgRating = 0

print(movieDict[1], '\n')
neighbors = getNeighbors(1, K) # Toy Story (1995)
for neighbor in neighbors:
    avgRating += movieDict[neighbor][3]
    print (movieDict[neighbor][0] + " " + str(movieDict[neighbor][3]))

avgRating /= K

Recommendation for ('Toy Story (1995) :

('Toy Story (1995)', array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 0.77358490566037741, 3.8783185840707963)

Liar Liar (1997) 3.15670103093
Aladdin (1992) 3.81278538813
Willy Wonka and the Chocolate Factory (1971) 3.63190184049
Monty Python and the Holy Grail (1974) 4.0664556962
Full Monty, The (1997) 3.92698412698
George of the Jungle (1997) 2.68518518519
Beavis and Butt-head Do America (1996) 2.78846153846
Birdcage, The (1996) 3.44368600683
Home Alone (1990) 3.08759124088
Aladdin and the King of Thieves (1996) 2.84615384615

While we were at it, we computed the average rating of the 10 nearest neighbors to Toy Story:

avgRating

3.3445905900235564

How does this compare to Toy Story's actual average rating?

movieDict[1]

('Toy Story (1995)',
 array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
 0.77358490566037741,
 3.8783185840707963)

Not too bad!