Anime Recommendation System
This project explores various recommendation system techniques for anime recommendations using data from MyAnimeList. The project implements three main approaches: Collaborative Filtering (recommendations based on user preferences and similarities with other users), Content-Based Filtering (recommendations based on product descriptions and similarity between items), and User Similarity-Based recommendations (based on user profile similarities). The project uses the Surprise library for collaborative filtering algorithms including KNN variants, SVD, SVD++, SlopeOne, CoClustering, and SGD-based models. It includes comprehensive evaluation metrics (MAE, RMSE, Hit Rate, ARHR, User Coverage), hyperparameter tuning with GridSearchCV, and systematic model comparison.
Overview
This project explores various recommendation system techniques for anime recommendations using data from MyAnimeList. The project implements three main approaches: Collaborative Filtering (recommendations based on user preferences and similarities with other users), Content-Based Filtering (recommendations based on product descriptions and similarity between items), and User Similarity-Based recommendations (based on user profile similarities). The project uses the Surprise library for collaborative filtering algorithms including KNN variants, SVD, SVD++, SlopeOne, CoClustering, and SGD-based models. It includes comprehensive evaluation metrics (MAE, RMSE, Hit Rate, ARHR, User Coverage), hyperparameter tuning with GridSearchCV, and systematic model comparison.
Key Features
Collaborative filtering with 9+ algorithms (KNN variants, SVD, SVD++, SlopeOne, CoClustering, SGD)
Content-based filtering for product similarity
User similarity-based recommendations
Comprehensive evaluation metrics (MAE, RMSE, Hit Rate, ARHR, User Coverage)
Hyperparameter tuning with GridSearchCV
Model persistence and comparison
Large-scale dataset handling (1.1GB+ MyAnimeList data)
Sparse matrix optimization
Top-N recommendation function
Statistical analysis and visualization
pages.portfolio.projects.anime_recommendation_system.features.10
Technical Highlights
Implemented 9+ collaborative filtering algorithms using Surprise library
Created comprehensive evaluation framework with multiple metrics
Performed hyperparameter tuning with GridSearchCV
Handled large-scale MyAnimeList dataset (1.1GB+)
Compared collaborative, content-based, and user similarity approaches
Developed top-N recommendation function with personalized results
Challenges and Solutions
Data Sparsity
Addressed highly sparse user-item matrix using threshold-based filtering and matrix factorization
Cold Start Problem
Implemented content-based filtering and hybrid approaches for new users/items
Scalability
Used efficient data structures, sparse matrices, and optimized algorithms for large dataset
Hyperparameter Tuning
Applied GridSearchCV for systematic hyperparameter search and performance tracking
Evaluation Metrics
Developed comprehensive metric suite covering accuracy, ranking, and coverage perspectives
Model Comparison
Created standardized evaluation framework for systematic algorithm comparison
Technologies
Recommendation Systems
Data Processing
Visualization
Analysis
Evaluation
Environment
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- Multi-Approach Recommendation System with Collaborative, Content-Based, and User Similarity Methods
- Category
- Data Science