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Python project advanced: Movie Recommendation System using Collaborative Filtering

Python project advanced: Movie Recommendation System using Collaborative Filtering: here’s an advanced Python project with code. This project involves building a recommendation system using collaborative filtering.

Objective of Python project advanced

To build a movie recommendation system that suggests movies to users based on their previous ratings and the ratings of similar users.

Tools and Libraries Used:

  • Python 3.6+
  • Pandas
  • Scikit-learn

Steps of the Python project advanced

  1. Load the data into a Pandas dataframe:
import pandas as pd

ratings_data = pd.read_csv('ratings.csv')
movies_data = pd.read_csv('movies.csv')
  1. Merge the two dataframes:
movie_ratings = pd.merge(ratings_data, movies_data, on='movieId')
  1. Group the movie ratings by user ID:
user_ratings = movie_ratings.groupby(['userId', 'title'])['rating'].max().unstack()
  1. Fill in missing ratings with 0:
user_ratings = user_ratings.fillna(0)
  1. Calculate the user similarity matrix using cosine similarity:
from sklearn.metrics.pairwise import cosine_similarity

user_similarity = cosine_similarity(user_ratings)
  1. Define a function to get similar users:
def get_similar_users(user_id, user_similarity_matrix, num_users=5):
    user_similarity = user_similarity_matrix[user_id]
    similar_users = user_similarity.argsort()[:-num_users-1:-1]
    return similar_users
  1. Define a function to recommend movies:
def recommend_movies(user_id, user_similarity_matrix, user_ratings, num_recommendations=5):
    similar_users = get_similar_users(user_id, user_similarity_matrix)
    recommendations = []
    for user in similar_users:
        movies = user_ratings.iloc[user]
        movies_not_watched = movies[movies == 0].index
        for movie in movies_not_watched:
            if movie in recommendations:
                continue
            rating = user_ratings.iloc[user][movie]
            if rating > 3:
                recommendations.append(movie)
            if len(recommendations) == num_recommendations:
                break
        if len(recommendations) == num_recommendations:
            break
    return recommendations
  1. Test the recommendation system:
user_id = 1
recommendations = recommend_movies(user_id, user_similarity, user_ratings)
print(recommendations)

9. Deploy the recommendation system:

A Python web framework like Flask or Django can be used to deploy the recommendation system as a web application.

10. Improve the recommendation system:

  • Using matrix factorization algorithms like Singular Value Decomposition (SVD) or Alternating Least Squares (ALS).
  • Incorporating contextual information like the time of day or the user’s location.
  • Using deep learning models like neural networks to learn non-linear relationships between users and movies.

Categories: Python

1 Comment

Binance账户 · March 19, 2024 at 12:32 pm

Your point of view caught my eye and was very interesting. Thanks. I have a question for you.

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