A repository for a machine learning project about developing a hybrid movie recommender system.

Objective of the project is to build a hybrid-filtering personalized news articles recommendation system which can suggest articles from popular news service providers based on reading history of twitter users who share similar interests (Collaborative filtering) and content similarity of the article and user’s tweets (Content-based filtering).

A react native(UI), FastAPI (Server) and MySQL(DB) non-fungible token market place with a machine learning content-based filtering recommendation engine.

Movie Website built on python Django framework; Uses Content Based Predictive Model approach to predict similar movies based on the contents/genres similarities

Collaborative Filtering NN and CNN based recommender implemented with MXNet

Code repo of solution of 11th place in Recsys Challenge 2022

Proyek akhir recommendation system untuk membangun model machine learning yang dapat memberi top-N anime rekomendasi

Posts/Feeds recommendation engine based on content based and collaborative filtering methods

A python notebook for building collaborative, content-based, and ml-based recommender systems with Sklearn and Surprise

Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows

Comparison of performance evaluation of the baseline and hybrid recommendation systems using various metrics, to prove that hybrid systems perform better

Recommending movies to user using various Colaborative Filtering and Content Based Filtering.

Recommendation System for Amazon Alexa E-Commerce Application

Recommending Recipes with Content-based Filtering Approach (based on nutritions)

This project associated with my university for milestone project. A book recommender system using k-means clustering with content based approach from goodreads book dataset.

A recommendation system for books. Built by following two filtering methods that are Collaborative Filtering and Content Based Filtering. Algorithms used are KNN, Pearson Correlation, and TF-IDF. Every dataset used can be easily found in the data folder of the respository.

Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi

Reference repository for the O-Horizon Recommendation Engine featured in Neo4j Graphversation Episode 2

Movie Recomendation System is a movie recommender system using the TMDB 5000 Movie Dataset on Kaggle. Main goal of this system is to develop essential skills in data handling, exploratory data analysis, and model building