Recommendation System using ML and DL
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Updated
Dec 8, 2022 - Jupyter Notebook
Recommendation System using ML and DL
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.
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.
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"
This repository contains all the code used in the Recommender System challenge of the Recommender System exam at PoliMi.
🎵 A hybrid recommendation system for Amazon Digital Music (2023) combining TF-IDF, collaborative filtering, and popularity models into an interactive Streamlit app.
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.
"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.
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.
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