Movie-Recommender-Systems

This repository contains 4 different recommender systems for movies based on user ratings.

1. Movie based Collaborative Filtering Recommender System
2. Singular Value Decomposition Recommender System
3. Latent Factor Model of Recommender System
4. CUR based Recommender System

These are made in Python language using a dataset of 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 users.

RATINGS FILE DESCRIPTION

All ratings are contained in the file "ratings.dat" and are in the following format:

UserID::MovieID::Rating::Timestamp

• UserIDs range between 1 and 6040
• MovieIDs range between 1 and 3952
• Ratings are made on a 5-star scale (whole-star ratings only)
• Timestamp is represented in seconds since the epoch as returned by time(2)
• Each user has at least 20 ratings

USERS FILE DESCRIPTION

User information is in the file "users.dat" and is in the following format:

UserID::Gender::Age::Occupation::Zip-code

All demographic information is provided voluntarily by the users and is not checked for accuracy. Only users who have provided some demographic information are included in this data set.

• Gender is denoted by a "M" for male and "F" for female

• Age is chosen from the following ranges:

  • 1: "Under 18"
  • 18: "18-24"
  • 25: "25-34"
  • 35: "35-44"
  • 45: "45-49"
  • 50: "50-55"
  • 56: "56+"

• Occupation is chosen from the following choices:

  • 0: "other" or not specified
  • 1: "academic/educator"
  • 2: "artist"
  • 3: "clerical/admin"
  • 4: "college/grad student"
  • 5: "customer service"
  • 6: "doctor/health care"
  • 7: "executive/managerial"
  • 8: "farmer"
  • 9: "homemaker"
  • 10: "K-12 student"
  • 11: "lawyer"
  • 12: "programmer"
  • 13: "retired"
  • 14: "sales/marketing"
  • 15: "scientist"
  • 16: "self-employed"
  • 17: "technician/engineer"
  • 18: "tradesman/craftsman"
  • 19: "unemployed"
  • 20: "writer"

MOVIES FILE DESCRIPTION

Movie information is in the file "movies.dat" and is in the following format:

MovieID::Title::Genres

• Titles are identical to titles provided by the IMDB (including year of release)

• Genres are pipe-separated and are selected from the following genres:

  • Action
  • Adventure
  • Animation
  • Children's
  • Comedy
  • Crime
  • Documentary
  • Drama
  • Fantasy
  • Film-Noir
  • Horror
  • Musical
  • Mystery
  • Romance
  • Sci-Fi
  • Thriller
  • War
  • Western

• Some MovieIDs do not correspond to a movie due to accidental duplicate entries and/or test entries

• Movies are mostly entered by hand, so errors and inconsistencies may exist

Results obtained:

Collaborative without baseline approach:

RMSE: 1.33 Time taken: 16 secs

Collaborative with baseline approach:

RMSE: 1.14 Time taken: 16 secs

SVD:

RMSE: 0.91 Time taken: 28 secs

SVD with 90% retained energy:

RMSE: 0.93 Time taken: 28 secs

CUR

RMSE: 2.20 Time taken: 10 secs

CUR with 90% retained energy:

RMSE: 2.25 Time taken: 10 secs

Latent Factor Model:

RMSE: 0.90 Time taken: 32 secs secs