Repository of the python scripts for the CS competition held in Kaggle obtaining the 4th place
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Updated
Jan 2, 2019 - Jupyter Notebook
Repository of the python scripts for the CS competition held in Kaggle obtaining the 4th place
The learning material and projects for the ML algorithms in supervised and unsupervised methods such as Regression, Classification, Clustering and recommender Systems.
Project of my master thesis (Online Car Recommender system)
A python implementation of a hybrid semantic-based collaborative filtering recommender systems.
Demo is available at https://huggingface.co/spaces/quyanh/Book-Recommender-System
Game Recommendation using Collaborative filtering with K-Nearest Neighbor
in this section will be user based recommender on movies and ratings dataset
TMDB_5000_Movie_recommendation_system is a repository for a hybrid movie recommendation system. Discover personalized movie recommendations based on user preferences and movie features using the TMDB 5000 Movies dataset.
Create A Recommendation Engine For Blog Articles
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 IBM articles
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
This repo has an implementation of popular recommendation techniques like user-based and item-based collaborative filtering techniques for recommending books and music.
An Anime Recommendation System based on User-based Collaborative Filtering technique and KNN(Euclidean Distance) algorithm.
Recommender systems
This repository contains exploratory data analysis, user-based collaborative recommendation system study, and item-based collaborative recommendation system study on the dataset with 1.5 Million reviews of Beer from Beer Advocates. Topics
Recommendation System for Appliances, along with Topic Modelling and Sentiment Analysis
Sushi Recommender System!
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