hybrid-recommender-system

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.

A recommender system built for book lovers.

This repository contains the code for building movie recommendation engine.

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

Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.

A Hybrid recommendation engine built on deep learning architecture, which has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor

Hybrid book recommendation system fusing a scikit-learn k-nearest-neighbors collaborative filtering model with a TF-IDF cosine-similarity content model, weighted-average combined and trained on the 1.1 million rating Book-Crossing dataset, served through a FastAPI backend with TTL caching, a decoupled React frontend, and Docker Compose deployment.

This is a book recommendation engine built using a hybrid model of Collaborative filtering, Content Based Filtering and Popularity Matrix.

Hybrid recommedation for talents

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

A Content Based And A Hybrid Recommender System using content based filtering and Collaborative filtering

Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi

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

Movie recommendation system based on hybrid recommender and clustering

Architected a polyglot e-commerce platform integrating a React/RTK frontend, a Node.js backend, and a Python ML microservice. The platform's core is a hybrid, real-time recommendation system using Redis and pre-computed models to provide instant, personalized suggestions. The system is built for production with Stripe, AWS S3 & robust security

This repository contains the core model we called "Collaborative filtering enhanced Content-based Filtering" published in our UMUAI article "Movie Genome: Alleviating New Item Cold Start in Movie Recommendation"

This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.

Recommendation System Algorithm

Built a hybrid recommendation system with LightFM library and customised loss functions to optimize performance on retail data."

Auto encoders based recommendation system