metadata

title: Movie Recommender System
emoji: 🌖
colorFrom: yellow
colorTo: yellow
sdk: streamlit
sdk_version: 1.40.2
app_file: app.py
pinned: false

Movie Recommender System

Project Overview

This is a content-based movie recommender system built using Python, Pandas, and Streamlit. It uses vectorization techniques to recommend movies based on their features like genres, overview, and cast.

Features

Content-based filtering for personalized movie recommendations.
Interactive web interface built using Streamlit.
Fetches movie posters dynamically using the IMDb API.
Deployed on Hugging Face Spaces.

Dataset

Dataset: TMDB Movie Metadata
Contains metadata for up to 5000 movies, including genres, overview, cast, and crew.

Technologies Used

Python: For data processing and building the recommendation logic.
Pandas: For dataset manipulation.
Streamlit: For creating the web app.
Vectorization: To compute movie similarities using techniques like TF-IDF.
IMDb API: To fetch movie posters dynamically, enriching the user experience.

Modules and Their Roles

1. Data Loading and Preprocessing

This module is responsible for loading the TMDB dataset and preparing it for use.
Tasks include handling missing values, cleaning data, and extracting relevant features like genres, overview, and cast.

2. Vectorization Module

Converts text-based features such as movie overviews into numerical vectors using techniques like TF-IDF (Term Frequency-Inverse Document Frequency).
This module computes similarity scores between movies, which are critical for generating recommendations.

3. Recommendation Engine

This core module applies content-based filtering logic.
It uses the similarity scores to identify and recommend movies that are most similar to a user's selected movie.

4. Web Interface

Built with Streamlit, this module provides an interactive and user-friendly interface.
Users can search for a movie, view its details, and get personalized recommendations instantly.
Fetches movie posters using the IMDb API, making recommendations visually engaging.

Deployment

The project is deployed on Hugging Face Spaces. You can explore it here:
Movie Recommender System

Like this Hugging Face space? Feel free to try it out and share your feedback!

Future Enhancements

Add collaborative filtering to incorporate user behavior into recommendations.
Use additional APIs like TMDB for real-time movie metadata updates.
Improve scalability to handle larger datasets.

API Reference

This project integrates the IMDb API from RapidAPI to fetch movie posters dynamically. For more details on the API, check out the IMDb API documentation.

Acknowledgments

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference