There are 4 repositories under user-based-recommendation topic.
Semester project for Tishreen university.
deep learning project
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
Movie Recommendation using Matrix Factorisation, User based collaborative and Item based collaborative filtering
Book Recommendation Service
Association Rule Learning, Content Based Recommendation, Item Based Collaborative, Filtering User Based Collaborative Filtering, Model Based Matrix Factorization projects i've done about
User-based collaborative filtering movie recommender using MovieLens dataset
Implementing user-based and item-based collaborative filtering algorithms on MovieLens dataset and comparing the results.
Collaborative recommendation engine model for product similarity estimation
Books recommendation system by collaborative filtering and certain visualization are done on data.
This repository contains introductory notebooks for recommendation system.
Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.
Personalised and popularity-based movie recommendations.
A hybrid movie recommendation system
Book_Recommendation_Project
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)
In this repository, I implement a recommender system using matrix factorization. Here, two types of RS are implemented. First, use the factorized matrix for user and item. and second, rebuild the Adjacency matrix. both approaches are acceptable and implemented in this repo. To factorized the matrix, funk-svd Algorithm is used. you can find his implementation on this link: https://github.com/gbolmier/funk-svd
A simple movie recommender system that uses two main approaches to make recommendations: Content-based algorithm and Collaborative filtering algorithm (User-based).
Book recommendation system using user base collaborative filter Algorithm and testing the accuracy result by comparing with different algorithms
Recommendation algorithms
An article recommender system for IBM Watson based on User preferences and articles clicked.
Sushi Recommender System!
Building a collaborative filtering recommender systems on books dataset
Cryptocurrency Recommendation based on Tweets
A study on the naive user-based collaborative filtering algorithm and related improvements on the Movielens dataset.
Used User-based and Item-based Collaborative Filtering techniques to build a personalized Book Recommendation System
Recommender system for board games built on data collected from major board game forum, BoardGameGeek.
easy use user based collaborative filtering recommender system
In this section, I will create a user-based recommender on the movie dataset
An application that uses the algorithm of user-based collaborative filtering and item-based collaborative filtering to recommend new movies
Using the MovieLens 20 Million review dataset, this project aims to explore different ways to design, evaluate, and explain recommender systems algorithms. Different item-based and user-based recommender systems are showcased as well as a hybrid algorithm using a modified page-rank algorithm.
Recommendation System for Appliances, along with Topic Modelling and Sentiment Analysis