Transforming skincare recommendations: our hybrid system combines KNN, CNN, and EfficientNet B0 for personalized advice. Published in IEEE, with 80% validation accuracy and 87.10% training accuracy.

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

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

Collaborative Filtering NN and CNN based recommender implemented with MXNet

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

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

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

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

recommending recipes with content-based filtering approach

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

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

Recommendation System for Amazon Alexa E-Commerce Application

Design and implementation from scratch of different models for a musical recommendation system

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.

DS307.N11 - Hệ Khuyến Nghị