Dataset: Movie Recommendation System

About this Dataset

Movie Recommendation System

The Movie Recommendation System is a machine learning-based application that provides personalized movie recommendations to users. It utilizes collaborative filtering techniques to analyze user preferences and similarities among movies to generate accurate and relevant recommendations. The system is built using Python programming language and incorporates popular machine learning libraries such as scikit-learn and pandas.

The project utilizes the MovieLens dataset, a widely used dataset in the field of recommender systems, containing movie ratings and metadata. The dataset is preprocessed to create a user-item matrix and to calculate item similarity using cosine similarity. This enables the system to identify movies that are similar to the ones the user has previously enjoyed and recommend them accordingly.

The recommendation process involves taking a user's unique identifier as input and generating a list of top-rated movie recommendations specifically tailored to their preferences. The system dynamically adjusts and updates the recommendations as new data becomes available.

The Movie Recommendation System is intended for individuals who seek personalized movie suggestions to enhance their movie-watching experience. It can be integrated into various platforms such as streaming services, movie review websites, or personal movie catalog applications.

Tables

Movies

@kaggle.parasharmanas_movie_recommendation_system.movies

1.7 MB
62423 rows
3 columns


CREATE TABLE movies (
  "movieid" BIGINT,
  "title" VARCHAR,
  "genres" VARCHAR
);

Ratings

@kaggle.parasharmanas_movie_recommendation_system.ratings

154.97 MB
25000095 rows
4 columns


CREATE TABLE ratings (
  "userid" BIGINT,
  "movieid" BIGINT,
  "rating" DOUBLE,
  "timestamp" BIGINT
);