ANIREC: Anime Recommender System

ANIREC is a hybrid anime recommender system utilizing a multi-armed bandit algorithm. This project aims to provide personalized anime recommendations by combining content-based filtering and collaborative filtering techniques.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Code Structure
• How It Works
• Contributing
• License
• Contact

Overview

ANIREC is a sophisticated recommender system designed to provide accurate and relevant recommendations based on user preferences. This project combines multiple techniques, including content-based filtering and reinforcement learning, to deliver high-quality recommendations.

The recommender system is implemented as a command-line interface and integrates Word2Vec embeddings for semantic similarity, along with a similarity matrix and a reinforcement learning algorithm for further optimization.

Features

• Content-Based Filtering: Utilizes Word2Vec embeddings to understand and compute similarities between items.
• Similarity Matrix: Employs cosine similarity to determine item relevance based on embeddings.
• Hybrid Approach: Combines content-based and collaborative filtering techniques.
• Multi-Armed Bandit Algorithm: Uses the Upper Confidence Bound (UCB) method to refine recommendations based on user interactions.
• Command-Line Interface: Operates through a CLI for ease of use and integration with other systems.

Installation

1. Clone the Repository:

   git clone https://github.com/Mridul-23/Anirec.git
   cd Anirec

2. Set Up a Virtual Environment (Optional but Recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Required Packages:

   pip install -r requirements.txt

Usage

1. Prepare Data:

   Ensure that your dataset is in the required format. Place your data files in the appropriate directory or update the paths in the notebooks and script.

2. Run the Notebooks:

   Execute the following Jupyter notebooks in order to prepare and process the data, train the embeddings, and compute similarity:

   • Data Preprocessing: Data_Preprocessing.ipynb
   • Word2Vec Training: w2v model.ipynb
   • Similarity Matrix Computation: similarity_matrix.ipynb

   You can run these notebooks in a Jupyter environment.

3. Run the Recommender System:

   After preparing and processing the data, run the recommender system logic using:

   python model.py

   Follow the on-screen instructions to interact with the recommender system.

Code Structure

• Data_Preprocessing.ipynb: Contains data preprocessing logic.
• w2v model.ipynb: Handles Word2Vec embeddings training.
• similarity_matrix.ipynb: Computes item similarity using a similarity matrix.
• model.py: Contains the Multi-Armed Bandit final recommender system logic and model.
• requirements.txt: Lists Python dependencies required to run the project.

How It Works

1. Data Preparation: Data is preprocessed and converted into a format suitable for the recommender system.
2. Embedding Generation: Word2Vec embeddings are generated to capture semantic similarities between items.
3. Similarity Computation: A similarity matrix is created using cosine similarity.
4. Recommendation Generation: The Multi-Armed Bandit algorithm with UCB refines recommendations based on user interactions and item similarities.

Contributing

Contributions to the ANIREC recommender system are welcome! Please follow the standard GitHub workflow:

1. Fork the repository.
2. Create a feature branch.
3. Commit your changes.
4. Push to your forked repository.
5. Submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions or feedback, please reach out to:

• Author: Mridul Narula
• GitHub: Mridul-23