USC DSCI 553 - Foundations & Applications of Data Mining - Spring 2024 - Prof. Wei-Min Shen

Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.

Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.

A movie recommender application

A hybrid group recommendation system for film and TV content using Letterboxd profile data

Amar deep architectures for hybrid recommenders with GNNs

The goal of this project is to implement a Hybrid Recommender System that combines item-based and user-based recommendation methods to provide movie recommendations for a specific user. The system aims to offer a total of 10 movie recommendations by using both methods.

Hybrid Recommender System for Computer Science Papers | Master's Thesis Project 2023

This repository houses the codebase for a Book Recommendation System, crafted using collaborative filtering, Flask, and cosine similarity. The system employs advanced machine learning techniques to generate personalized book recommendations based on user preferences.

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

🎓 Final Project for Completing Bachelor Degree in Petra Christian University. Create Hybrid Recommender System for Interior Products and its Services using Data Implicit Feedback

The project is based on a Hybrid recommendation engine that uses both Collaborative as well as Content based filtering methods to suggest streamers to the online users based on the type content they consume.

A Hybrid Recommendation system which uses Content embeddings and augments them with collaborative features. Weighted Combination of embeddings enables solving cold start with fast training and serving

An hybrid recommender systems for suggesting medical therapies, based on matrix factorization and collaborative filtering.

Recommends movies using Collaborative and Content based filtering techniques

Recommender system challenge @ Polimi

Amar deep architectures for hybrid recommenders with GNNs

The objective of the competition was to create the best recommender system for a book recommendation service by providing 10 recommended books to each user. The evaluation metric was MAP@10.

Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi

Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi