Recommendation System using ML and DL

Accompanying code for reproducing experiments from the HybridSVD paper. Preprint is available at https://arxiv.org/abs/1802.06398.

Hybrid recommendation engine using deep learning that incorporates user and item features, including images and text.

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"

A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.

MelodyMind offers personalized music recommendations, from a real-time Last.fm API-powered app to an advanced hybrid system combining content-based and collaborative filtering with LightFM.

🎵 A hybrid recommendation system for Amazon Digital Music (2023) combining TF-IDF, collaborative filtering, and popularity models into an interactive Streamlit app.

This repository contains all the code used in the Recommender System challenge of the Recommender System exam at PoliMi.

Multi-algorithm recommendation system: collaborative filtering (ALS, SVD), content-based, hybrid with cold-start handling, A/B evaluation framework

Hybrid book recommendation system using NLP (TF-IDF, cosine similarity) and collaborative filtering (SVD) to generate personalized book suggestions with high accuracy. Includes full EDA, model evaluation.

A Hybrid Anime Recommender System using content-based and collaborative filtering, built with end-to-end MLOps practices. Integrates Comet-ML for experiment tracking, DVC for data/model versioning, Jenkins for CI/CD, and Kubernetes for scalable deployment.

🍽️ ML-powered restaurant recommendation system that reduces decision fatigue by 40%. Hybrid approach combining collaborative filtering, content-based filtering, and contextual signals. Complete PM portfolio project with PRD, evaluation, and Streamlit demo.

MOVIE-PULSE-HYBRID-INTELLIGENCE-MATRIX: movie recommendation engine blending NLP-driven Content Filtering and SVD Matrix Factorization. Features Streamlit Enterprise UI, dual-strategy processing, real-time logic analytics

A full-stack hybrid book recommender system combining collaborative filtering (ALS), content-based similarity (SBERT), and machine learning ranking (CatBoost). Backend: FastAPI • Frontend: Streamlit • Vector DB: Qdrant • Model serving • Cold-start fallback • Book metadata display.

Movie recommendation system with Python. Implements content-based filtering (TF-IDF + cosine similarity), collaborative filtering with matrix factorization (TruncatedSVD), and a hybrid approach. Evaluates with Precision@K, Recall@K, and NDCG. Includes rating distribution plots, top movies, and sample recommendations.

Hybrid Movie Recommender est une application full-stack qui génère des recommandations personnalisées de films en fusionnant des signaux content-based et collaborative filtering. Le projet comprend une API (Python), une interface web (TypeScript) et des scripts de préparation/gestion des données.

EduMeet is a full-stack MERN application integrated with a Flask-based Machine Learning microservice that provides intelligent teacher appointment booking and hybrid subject-based teacher recommendations using ML and rule-based logic.

"A hybrid recommendation system enhancing personalized music suggestions using collaborative and content-based filtering."

A lightweight recommender that helps you discover your next learning resource. It blends patterns from similar users with content keywords, and explains each suggestion in the UI.