The document outlines a movie recommendation system developed by a group as part of a final project, highlighting the necessity and methodologies including content-based and collaborative filtering approaches. It utilizes data from MovieLens to analyze genres, ratings patterns, and user behavior, ultimately recommending singular value decomposition (SVD) as a preferred model due to its balanced effectiveness. The findings indicate that while various models exist, user preferences and the data utilized significantly influence the output of recommendations.

1. MOVIE
RECOMMENDATION
SYSTEM
BANA7047-002 FINAL PROJECT
• Group 2
• Jagruti Joshi
• Priya Kumari
• Pooja Sahare
1

2. Part 1
• Background
• Data
• Preliminary Analysis
2

3. Background
Top Streaming
Services
Need for a
recommendation system 13,000+ titles ~8 seconds
Netflix’s total content library Average human attention span
Data used in a
recommendation system
Impact of a
recommendation system
Watch
Data
Search
Data
Ratings
Data
Increased
Revenues
3

4. Data
• Source: https://grouplens.org/datasets/movielens/
• recommended for education and development
• Small: 100,000 ratings and 3,600 tag applications applied to 9,000 movies by
600 users. Last updated 9/2018.
•Movies
•movieId
•title
•genres
•Ratings
•userId
•movieId
•rating
•timestamp
•Tags
•userId
•movieId
•tag
•timestamp
•Links
•movieId
•imdbId
•tmdbId
9,742 100,836 1,587,923 9,742
4

5. Preliminary Analysis
 Data
• Movies
 Insights
• No of movies
released is
increasing every
year peaking at
733 in 2006.
• Sharp decline is
observed in
recent years
(could be due to
not including
recent releases in
the data)
5

6. Preliminary Analysis
 Data
• Movies
 Insights
• Drama, Comedy,
Thriller, Action
and Romance are
the top 5 genres
• The top 5 genres
together contain
~60% of the
movies
6

7. Preliminary Analysis
 Data
• Movies
 Insights
• Drama and
Comedy have
consistently
stayed the #1 and
#2 genres over
the years
• Similar
distribution of
genres over the
years
7

8. Preliminary Analysis
 Data
• Movies
• Ratings
 Insights
• Median > Mean
• Left skewed
distribution
• Do most users
rate most movies
on the higher end
of the 0-5 scale?
8

9. Preliminary Analysis
 Data
• Movies
• Ratings
 Insights
• Most users rate
less than 1000
movies
• Most users rate
movies on the
higher end of the
0-5 scale
• Most movies
receive less than
100 ratings
• Most movies are
rated on the
higher end of the
0-5 scale
9

10. Preliminary Analysis
 Data
• Movies
• Ratings
 Insights
• Average Ratings
for all genres is
between 3 and 4
• Among the top 5
genres, Drama
and Romance
movies have
higher ratings
compared to the
remaining three
#1 #2 #4#3#5
10

11. Preliminary Analysis
 Data
• Movies
• Tags
 Insights
• Tags can be useful
to create sub-
genres and drill
deeper into a
specific genre e.g.
Sci-Fi movies are
a huge sub-genre
within Action
movies
11

12. Part 2
• Content Based Filtering
• User Based Collaborative Filtering
• Item Based Collaborative Filtering
• Singular Value Decomposition
12

13. Model 1: Content Based Filtering (CBF) Approach
• Genre-based approach
• Without factoring release year, algorithm
recommends very old movies
• Term Frequency (TF) and Inverse Document
Frequency (IDF) used to determine the relative
importance of genres
• Vector Space Model
• Each movie represented by a vector of its
attributes
• For similar movies,
• Angle between their vectors is small
• Cosine of angle between their vectors is
large
13
1 movie
Movie Genre,
Release Year
CBF Algorithm
TF/IDF,
Cosine Similarity Score
n movies
Similar in Genres,
Closer in Release Years
Sort Results
Highest to Lowest
Cosine Similarity Scores
Filter Results
Select Top 20
Movies
Recommend Results
Display Movies in User’s
Watch Next List

14. Model 1: Content Based Filtering (CBF) Results
14
1. Rampage (2018)
2. Solo: A Star Wars Story (2018)
3. Ant-Man and the Wasp (2018)
4. Deadpool 2 (2018)
5. Sorry to Bother You (2018)
6. Pacific Rim: Uprising (2018)
7. A Wrinkle in Time (2018)
8. Jupiter Ascending (2015)
9. Avengers: Age of Ultron (2015)
10.Ant-Man (2015)
11.Power/Rangers (2015)
12.Turbo Kid (2015)
13.Hardcore Henry (2015)
14.Iron Man (2008)
15.Journey to the Center of the Earth (2008)
16.Mutant Chronicles (2008)
17.Outlander (2008)
18.Doctor Strange (2016)
19.Independence Day: Resurgence (2016)
20.Star Trek Beyond (2016)
Avengers: Infinity War - Part I (2018) Toy Story (1995) Insidious: Chapter 3 (2015)
1. Gordy (1995)
2. Reckless (1995)
3. Ninja Scroll (Jûbei ninpûchô) (1995)
4. Tale of Despereaux, The (2008)
5. Wild, The (2006)
6. Asterix and the Vikings (Astérix etlesVikings)
(2006)
7. Monsters, Inc. (2001)
8. The Good Dinosaur (2015)
9. Toy Story 2 (1999)
10.Shrek the Third (2007)
11.Moana (2016)
12.Adventures of Rocky and Bullwinkle,The-2000
13.Emperor's New Groove, The (2000)
14.Turbo (2013)
15.Antz (1998)
16.Jumanji (1995)
17.Indian in the Cupboard, The (1995)
18.Shrek (2001)
19.TMNT (Teenage Mutant Ninja Turtles)(2007)
20.Three Wishes (1995)
1. The Gallows (2015)
2. Frankenstein (2015)
3. Maggie (2015)
4. Body (2015)
5. Massu Engira Maasilamani (2015)
6. Into the Grizzly Maze (2015)
7. Return to Sender (2015)
8. Careful What You Wish For (2015)
9. Spotlight (2015)
10. Mojave (2015)
11. Knock Knock (2015)
12. Zipper (2015)
13. The Stanford Prison Experiment (2015)
14. Partisan (2015)
15. Bridge of Spies (2015)
16. The Perfect Guy (2015)
17. Silent Hill (2006)
18. Nightmare on Elm Street, A (2010)
19. Insidious (2010)
20. Paperhouse (1988)

15. Model 2: User-based Collaborative Filtering (UBCF)
Approach & Results
15
• Find look alike users based on similarity
• Recommend movies which user’s look-alike
has chosen in past.
• Very effective due to creation of user profiles
• Very time and resource consuming algorithm
as computations are made for every user pair.
Thus, we only take 20% of original data
• Results
User 1
• Avengers
• Age of Ultron
• Civil War
• Infinity War
• Iron Man
• Iron Man 2
• Iron Man 3
• Endgame
Not Watched
Watched
Watched
Recommend
Similar
Sample Data For Model 20% of Original Data
Model Train Data 80% of Sample Data
Model Test Data 20% of Sample Data
Root Mean Square Error 24167

16. Model 3: Item-based Collaborative Filtering (IBCF)
Approach & Results
16
• Like UBCF, but instead of finding user's look-
alike, we find a movie's look-alike.
• Recommend alike movies to user who has
rated this movie.
• Far less time and resource consuming than
UBCF but we’ve used the same 20% subset of
original data for model comparison
• Results
Watched
User 1 Avengers
• Age of Ultron
• Civil War
• Infinity War
• Endgame
Similar
Recommend
Sample Data For Model 20% of Original Data
Model Train Data 80% of Sample Data
Model Test Data 20% of Sample Data
Root Mean Square Error 29123

17. • Basic essence of SVD is to decomposes a
matrix of any shape into a product of 3
matrices with notable mathematical
properties: X = U S VT
• Decomposition of ratings matrix results in an
ordered matrix of a user feature matrix and
an item feature matrix which encapsulate the
variance associated with every direction of
the matrix
• Larger variances indicate less redundancy
and less correlation and hold features of data
• A representative subset of user rating
directions or principal components to
recommend movies is utilized
• Overall SVD aims to find the smallest
condensed subset of features by discarding
features imparting noise
17
Model 4: Singular Value Decomposition (SVD)
Approach
Movie
User
Sci-Fi
FemaleMale
Wonder
Woman
Captain
Marvel
Drama
Avengers
Endgame
Iron Man
Captain
America
Thelma &
Louise
Legally
Blonde
The
Shawshank
Redemption
Fight Club

18. Model 4: Singular Value Decomposition (SVD)
Results
Top rated movies by user ID 400
18
Recommended movies for user ID 400

19. Model Comparison & Recommendations
Model Proportion of Data RMSE
UBCF 20% 24167
IBCF 20% 29123
SVD 20% 0.91
19
• Movie recommendations are very subjective and vary from one user to another
• Each model has a different approach and its own set of pros and cons
• Weighing all the pros and cons, we would recommend SVD as it is a good mix of both collaborative filtering
methods

20. References
• Slide 2: Background
• https://www.comparitech.com/blog/vpn-privacy/netflix-statistics-facts-
figures/
• Slide 3: Data
• https://grouplens.org/about/what-is-grouplens/
• https://movielens.org/info/about
20

21. References
• Slides 11,13,15: Collaborative Filtering, UBCF and IBCF
• https://github.com/khanhnamle1994/movielens/blob/master/Content_Base
d_and_Collaborative_Filtering_Models.ipynb
• https://www.comparitech.com/blog/vpn-privacy/netflix-statistics-facts-
figures/
• Slide 17: SVD
• http://www.cs.carleton.edu/cs_comps/0607/recommend/recommender/svd.
html
• https://alyssaq.github.io/2015/20150426-simple-movie-recommender-using-
svd/
• https://www.dataminingapps.com/2020/02/singular-value-decomposition-in-
recommender-systems/
21