This repository contains a movie recommender system built using collaborative filtering techniques and vector space embedding. The system suggests movies based on user preferences by employing cosine similarity on a dataset of movies.
- Collaborative Filtering: The recommender system uses collaborative filtering techniques to suggest movies based on user preferences.
- Vector Space Embedding: Movies are represented in a vector space, allowing for efficient comparison and recommendation.
- Cosine Similarity: Cosine similarity is employed to determine the similarity between movies and recommend those most closely related to the user's preferences.
- Streamlit Integration: The recommender system is integrated into a Streamlit application, providing a user-friendly interface for interaction.
To get started with the movie recommender system, follow these steps:
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Clone the Repository: Clone this repository to your local machine using the following command:
git clone https://github.com/your-username/movie-recommender-system.git
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Install Dependencies: Navigate to the cloned directory and install the necessary dependencies by running:
pip install -r requirements.txt
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Run the Application: Launch the Streamlit application by executing the following command:
streamlit run app.py
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Interact with the Application: Access the application through your web browser and explore movie recommendations based on your preferences.
- Upon launching the Streamlit application, users will be presented with an interface to input their movie preferences.
- Users can specify movies they have enjoyed in the past or provide characteristics of movies they typically prefer.
- Based on the provided input, the recommender system will suggest similar movies using collaborative filtering and vector space embedding techniques.
- Users can explore the recommended movies and select options for further details or additional recommendations.
Contributions to the movie recommender system are welcome! To contribute, follow these steps:
- Fork the repository to your GitHub account.
- Create a new branch for your feature or bug fix.
- Commit your changes and push the branch to your fork.
- Submit a pull request to the main repository detailing the changes you've made.
This project is licensed under the MIT License. See the LICENSE file for details.
Special thanks to the developers of Streamlit for providing an excellent framework for building interactive web applications.
Feel free to reach out to the project maintainers for any questions, feedback, or suggestions regarding the movie recommender system. Thank you for your interest and contribution!