Movie Lens Movie Recommender Systems. Used content-based filtering and collaborative filtering to build the recommender systems

1. Recommender Systems -
Movie Lens
Team 3
Team Members:
Ting Cai
Chan Wu
Kapil Garg
Sukanya Tiwatne
Jailu Gu
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2. Agenda
Data and data preprocessing
Data visualization
No model approach for new users
Content based filtering using correlation
Collaborative filtering using user attributes
Q&A
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3. Data and Data Preprocessing
Data Source Link: https://grouplens.org/datasets/movielens/100k/
Reading files: Rating file, Item file, User file
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original data and files

4. Data and Data Preprocessing
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Users Information Rating Information
Movie information
Merge dataset
user_id and movie_id

5. Data and Data Preprocessing
Drop blank column
Genres of movie
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14. Why do these companies allow free subscription?
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15. How to recommend movies when you don’t know
anything about Customer?
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1. Past Movie Ratings 2. Popularity of the Movie

16. How Rating Impacts Recommendation?
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17. No Model Recommender System
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Step 1: Loading the Dataset into
python
Step 2: Merging the Datasets into
one
Step 3: Calculating count of ratings
and average of ratings
Step 4: Sorting the data based on
count and average of ratings
Step 5: Deciding the cutoff value for
count
Step 6: Recommending movies

18. Content based Recommender System
● Focus on attributes of item
● Recommendations based on item - similarity
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Movies seen by user
Similar Movies

19. What is the catch?
● Explore data to see best rated movies
● Most popular movies:
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20. ●
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21. Implementation
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22. Movies similar to Star Wars:
●Movies similar to Liar Liar:
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23. Approaches for old users
• Preference for the movie type
• Same Occupation
• Similar Age
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Movies seen by both users
Seen by her, Recommend
to him!

24. • Preference for the movie type
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25. • Same Occupation
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26. • Similar Age
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27. Basic Structure of the Code
Input dataset
For loop of User_id :
For loop to find preference type:
For loop to rate movies of different movie types:
For loop to rate movies of different occupations:
For loop to rate movies of different ages ( age between “age-5” and “age+5” ):
Output to CSV
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28. Output
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29. ● if row['user_id'] != uid
● if len(mrows) < 4:
continue
(60% - 70%)
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