Hybrid Recommendation System
Recommendation system project focused on delivering personalized user recommendations using collaborative filtering, content-based filtering, and popularity-based models.
Hybrid Recommendation System
Developed a hybrid recommendation system combining Collaborative Filtering, Content-Based Filtering, and popularity-based recommendation models to provide personalized user experiences and solve cold-start problems.
Key Features
- Hybrid Recommendation Architecture
- Collaborative Filtering
- Content-Based Filtering
- Popularity-Based Recommendation
- Cold-Start Problem Handling
- Personalized Recommendation Engine
Project Overview
This project focuses on building a recommendation system capable of generating personalized content recommendations based on user behavior, item similarity, and popularity metrics.
Multiple recommendation techniques were integrated into a hybrid architecture to improve recommendation quality, increase personalization performance, and address cold-start issues for new users and new items.