My_Anime_Recommender

Anime Recommendation System

Overview

This repository contains an Anime Recommendation System designed to provide personalized anime recommendations based on user preferences. Whether you’re an anime enthusiast or a newcomer, this system aims to enhance your viewing experience by suggesting anime titles that align with your tastes.

Features

• Collaborative Filtering: Utilizes collaborative filtering techniques (e.g., user-based or item-based) to recommend anime based on similar users’ preferences.
• Content-Based Filtering: Considers anime attributes (e.g., genre, studio, release year) to recommend titles with similar characteristics.
• Hybrid Approach: Combines collaborative and content-based methods for robust recommendations.
• User Interaction: Allows users to rate anime, update preferences, and receive real-time recommendations.

Getting Started

1. Data Collection:
   • Gather anime data from sources such as MyAnimeList, AniList, or Kaggle datasets.
   • Extract relevant information (e.g., titles, genres, ratings).
2. Data Preprocessing:
   • Clean and preprocess the data (handle missing values, remove duplicates).
   • Create user-anime interaction matrices.
3. Model Building:
   • Implement collaborative filtering algorithms (e.g., matrix factorization, nearest neighbors).
   • Develop content-based models using anime attributes.
   • Combine models to create hybrid recommendations.
4. User Interface:
   • Design a user-friendly interface for users to input preferences and receive recommendations.
   • Allow users to rate anime and update their profiles.

Usage

1. User Registration:
   • Users create profiles by providing their anime preferences (favorite genres, watched titles).
2. Recommendations:
   • Based on user profiles, recommend anime titles.
   • Display top recommendations with confidence scores.
3. Feedback Loop:
   • Users rate recommended anime.
   • Update user profiles with new interactions.

Evaluation

1. Metrics:
   • Evaluate recommendation quality using metrics like precision, recall, and F1-score.
   • Conduct A/B testing to compare different recommendation approaches.
2. User Satisfaction:
   • Collect user feedback to assess system performance.
   • Continuously improve recommendations based on user input.

Contributing

Contributions are welcome! If you have ideas for improving the recommendation system or want to add new features, feel free to open an issue or submit a pull request.

License

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