This project implements an Item-Based Collaborative Filtering book recommendation system using K-Nearest Neighbors (KNN). Books are recommended based on similarity in user rating patterns rather than content information.
A movie recommendation system that using collaborative filtering and matrix factorization
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
Streamlit Restaurant Recommender
A memory-based collaborative filtering system that predicts movie ratings using user–user and item–item similarity.
Product recommendation system is a Machine learning based project which can be used to proivde personalized recommendations.
Movie recommender system on MovieLens 100K using Item-CF, model-based CF, weighted ensembles, and long-tail reranking to balance accuracy and catalog coverage.
A C++ project that predicts movie ratings using Item-Based Collaborative Filtering (IBCF) with Cosine Similarity. Developed for CMP2003 at Bahçeşehir University.
Live web application demonstrating personalized recommendations for books and mangas implemented using collaborative filtering based recommender systems
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