A Python-based hybrid book recommendation system that combines content-based and collaborative filtering techniques. Utilizes the Book-Crossing dataset for personalized recommendations.

Recommendation App for Books and Manga: MANGOLEAF

A personalized movie recommendation system powered by PySpark using collaborative filtering to deliver spot-on suggestions based on user behavior . Built for scale. Made for binge-watchers.

A memory-based collaborative filtering system that predicts movie ratings using user–user and item–item similarity.

This project implements a news article recommendation system using collaborative filtering techniques. The system analyzes user interactions with various content items (news articles) to suggest new content that users might find interesting. The primary goal is to enhance user engagement by providing personalized recommendations.

Product recommendation system is a Machine learning based project which can be used to proivde personalized recommendations.

Live web application demonstrating personalized recommendations for books and mangas implemented using collaborative filtering based recommender systems

Evaluations and Comparisons of Recommendation Systems Using The MovieLens Dataset

This repo is a small, GitHub-ready Python project that demonstrates user-based collaborative filtering using a sample user–item interaction matrix and cosine similarity.

A minimal C++ implementation for user-based collaborative filtering on the MovieLens 10M dataset.

Movie recommender system using collaborative filtering and Flask web app.