https://github.com/niteshchawla/movie-recommender-system
To create a Recommender System to show personalized movie recommendations based on ratings given by a user and other users similar to them in order to improve user experience.
https://github.com/niteshchawla/movie-recommender-system
collaborative-filtering correlation-matrix cosine-similarity exploratory-data-analysis feature-engineering knearest-neighbor-algorithm mape matrix-factorization pca-analysis pearson-correlation recommender-system rmse sparsity tsne-visualization visualization
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To create a Recommender System to show personalized movie recommendations based on ratings given by a user and other users similar to them in order to improve user experience.
- Host: GitHub
- URL: https://github.com/niteshchawla/movie-recommender-system
- Owner: Niteshchawla
- Created: 2025-02-10T09:45:34.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-10T09:53:43.000Z (3 months ago)
- Last Synced: 2025-02-10T10:39:01.648Z (3 months ago)
- Topics: collaborative-filtering, correlation-matrix, cosine-similarity, exploratory-data-analysis, feature-engineering, knearest-neighbor-algorithm, mape, matrix-factorization, pca-analysis, pearson-correlation, recommender-system, rmse, sparsity, tsne-visualization, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Movie-Recommender-System
To create a Recommender System to show personalized movie recommendations based on ratings given by a user and other users similar to them in order to improve user experience.
Data Dictionary:
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
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
**Concepts Used:**
Recommender Engine
Collaborative Filtering (Item-based & User-based Approach)
Pearson Correlation
Nearest Neighbors using Cosine Similarity
Matrix Factorization