Project information
- Category: Recommender System
- Client: N/A
- Project date: 15 February, 2023
- Project URL: Movie Recommender
- Technology Stack Used: Python
Movie Recommendataion System
The content-based recommender system is a movie recommendation system implemented in Python using the Streamlit framework. The system utilizes a dataset of movies, stored in a Pandas DataFrame, and a similarity matrix that measures the similarity between movies. The recommender system allows users to select a movie from a dropdown menu and click a "Recommend" button to receive movie recommendations based on the selected movie. The recommendations are generated by calculating the cosine similarity between the selected movie and other movies in the dataset using the similarity matrix. The top 6 similar movies are then displayed along with their posters, fetched from The Movie Database API. The recommendations and corresponding posters are presented in a user-friendly interface using Streamlit's column layout. This recommender system provides personalized movie recommendations based on the content of the selected movie, helping users discover new movies of interest.