hybrid-recommender

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.

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.

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.

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.

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

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.

Production-style movie recommendation engine built with Hybrid Recommenders, FastAPI, Streamlit, and MovieLens. Features collaborative filtering, content-based recommendations, model evaluation, and interactive recommendations.

🎬 Hybrid Movie Recommendation System using Machine Learning, Streamlit, and TMDB API.

Hybrid recommendation system built with PyTorch, FastAPI, and PostgreSQL using Neural Matrix Factorization with collaborative and content-based filtering.

Explainable movie recommender on MovieLens: sentence-embedding, item-kNN and hybrid models, temporally evaluated, served as a static React site.

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

🍽️ 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.

A hybrid recommendation system that suggests personalized Spider-Man comics, movies, games, and shows using content-based filtering, collaborative filtering, user preferences, popularity signals, and offline ranking metrics.

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

Hybrid music recommender blending TF-IDF tag similarity and implicit ALS collaborative filtering on the Last.fm-2k dataset, with an evaluation harness and a Streamlit demo.