hybrid-recommendation

This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

A recommender engine built for a Bay Area online dating website to maximize the successful matches by introducing hybrid recommender system and reverse match technique.

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 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"

Hotel Recommendation system based on Content, Collaborative, Social Network Based Systems

A python based hybrid recommendation system built from scratch

This is the source code for my MSc thesis on Hybrid Recommendation Systems using Neural Networks.

Repository related to the project of the Data Mining graduate course of University of Trento, academic year 2022/2023.

An AI-native database designed for LLM applications, offering lightning-fast hybrid search across dense vectors, sparse vectors, multi-vector tensors, and full-text data.

Integrated student event management platform for UNSIKA featuring an AI-powered hybrid recommendation engine built with Next.js, Supabase, and pgvector.

A small neural net to recommend movies to the user

A implementation in Scala of CF, Content Based, Sequential and hybrid recommender systems for Spark

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.

Recommendation system. Probability. Model training. Machine Learning. Vector search with ChromaDB. Neural re-ranking on the Frontend with TensorFlow.js.

Hybrid recommendation engine combining collaborative filtering with sentiment-aware, intent-based NLP for highly personalized, adaptive movie discovery.

🛒 Build an e-commerce recommendation system using Flask and machine learning to deliver personalized product suggestions with multiple algorithms.

Ohara bookshelf is a smart books recommendation platform using machine learning algorithms and neural network.

🦾🤖🪽 2-HabitSync — an AI agent that gives couples voice-guided, explainable recommendations for training and diet products. Hybrid engine (content-based + simulated collaborative filtering) with hard safety-priority for allergies and dietary restrictions. Academic case study, 100% synthetic data.