Anime Recommendation System

This project aims to build an intelligent Anime Recommendation System using data scraped from various anime-related websites and user comments. The system preprocesses and analyzes user-generated content to derive meaningful insights, enabling personalized recommendations powered by machine learning and LLM-based retrieval-augmented generation (RAG).

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Table of Contents

• Overview
• Features
• Folder Structure
• Technologies Used
• Setup Instructions
• Usage
• Contributing
• License

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Overview

The Anime Recommendation System scrapes data from popular anime websites, including genres, names, ratings, and user comments from relevant threads. The comments are preprocessed, analyzed, and scored, which are then utilized by machine learning models for recommendations. Using a Retrieval-Augmented Generation (RAG) model, the system delivers highly personalized recommendations based on user queries and interest ratings.

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Features

• Data Scraping: Extracts comprehensive data from anime platforms, such as MyAnimeList, Reddit, and AniCrush.
• Comment Analysis: Preprocesses user comments to remove noise and extract meaningful insights for recommendations.
• Machine Learning: Employs scoring models to rank anime based on user preferences.
• LLM Integration: Utilizes RAG for query-based recommendations, enhancing user experience.
• Data Cleaning: Includes robust scripts for cleaning and preparing data for analysis.

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Folder Structure

Anime-Recommendation/
├── Build-Docs/
│   ├── DataPreparation.log          # Logs for data preparation process
│   ├── FileLogger.py                # Logging utilities
│   └── InstallHeaders               # Script for environment setup
├── Data-Preparation/
│   ├── Anicrush-data/
│   │   ├── AnimeListFromAnicrush.csv
│   │   ├── anicrushextradetails.py
│   │   └── get_animedetails_from_anicrush.py
│   ├── AnimeDataScrapper/
│   │   ├── AniScrapper.py
│   │   ├── MyAnimeList.py
│   │   ├── MyAnimeListData.csv
│   │   ├── RedditData.csv
│   │   └── RedditScrapper.py
├── Data-Preprocessing/
│   ├── pre_process_for_comment_analysis.py
│   ├── pre_processing.py
├── ML-Files/
│   ├── LLMWithCSVandVectorDB.ipynb   # Jupyter notebook for LLM integration
│   ├── score_generation.py
│   ├── score_generation_analysis.ipynb