Semester project for Tishreen university.

Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.

Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.

deep learning project

Movie Recommendation using Matrix Factorisation, User based collaborative and Item based collaborative filtering

Book Recommendation Service

Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity

User-based collaborative filtering movie recommender using MovieLens dataset

Association Rule Learning, Content Based Recommendation, Item Based Collaborative, Filtering User Based Collaborative Filtering, Model Based Matrix Factorization projects i've done about

Collaborative recommendation engine model for product similarity estimation

This repository contains introductory notebooks for recommendation system.

Books recommendation system by collaborative filtering and certain visualization are done on data.

Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.

Personalised and popularity-based movie recommendations.

An application that recommends music on the basis of previous heard songs of a user using a ML model. Using Collaborative-based filtering to recommend other songs similar to what the user likes. Download Data set from Kaggle (Million song data set)

A hybrid movie recommendation system

Book_Recommendation_Project

Used User-based and Item-based Collaborative Filtering techniques to build a personalized Book Recommendation System

Recommendation algorithms