The second project of the EPFL Machine Learning course, which aims to construct a Recommender System with good predictive performance using various Machine Learning Algorithms. (2018-2019)

Paper Review of Recommender Systems

Communication-Efficient Stratified Stochastic Gradient Descent for Distributed Matrix Completion

DST approach on Recommended Systems(RS).

A library of recommender systems with collaborative, content-based filtering, and hybrid models.

In- and post- process methods for optimizing explanations path based on newly defined quantitative explanation metrics

A one-stop site for discovering and getting recommendations on Movies & TV Shows.

Collection of Context Aware Collaborative Filtering Datasets

This repository is for study recommender systems.

A movie recommendation engine

Propose and implement a machine learning pipeline that combines content-based and collaborative recommendation methods for a large-scale, personalized song recommendation system. The goal is to predict which songs that a user will listen to and make a recommendation list of 10 songs to each user, given both the user’s listening history and full …

Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback. I have applied basic content-based recommendation system using python.

This repository explores the use of tensorrec library in python to make a recommendation system for anime.

Dieses Repository enthält die Lösungen für die Übungsaufgaben der Vorlesung Recommendersysteme am Karlsruher Institut für Technologie aus dem SS 2020.

Implementation of model-based and memory-based collaborative filtering to predict movie rating

Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi

Challenge for the "Recommender Systems" course at Politecnico di Milano - AY 2023/2024

A personality-aware group recommendation system based on pairwise preferences

Built a recommender system, recommending the best products for eCommerce shoppers based on their purchase and search history

C++ implementation of GASGD