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.
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
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.
A implementation in Scala of CF, Content Based, Sequential and hybrid recommender systems for Spark
Sistema de recomendação . Probabilidade. Treinamento de Modelo. Machine Learning. Busca vetorial com ChromaDB. Re-ranking neural no FrontEnd com TensorFlow.js.
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.
🛒 Build an e-commerce recommendation system using Flask and machine learning to deliver personalized product suggestions with multiple algorithms.
Hybrid recommendation engine combining collaborative filtering with sentiment-aware, intent-based NLP for highly personalized, adaptive movie discovery.
A Spotify Music Recommender System that uses a hybrid recommendation approach (combining content-based scoring with track popularity) to suggest personalized music tracks through a Streamlit-based interactive app.
Ohara bookshelf is a smart books recommendation platform using machine learning algorithms and neural network.
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