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.
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Updated
Jan 25, 2018 - Python
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.
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 python based hybrid recommendation system built from scratch
A small neural net to recommend movies to the user
Repository related to the project of the Data Mining graduate course of University of Trento, academic year 2022/2023.
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.
🚗This GitHub repository hosts a project focused on car recommendations using content-based and collaborative filtering algorithms.
The assignment comprises two main tasks: implementing LSH to identify similar businesses based on user ratings and developing various collaborative filtering recommendation systems to predict user ratings for businesses.
This project developed and optimized a hybrid recommendation system that processes over 450,000 training data points and 142,000 validation data points. The system combines user ratings, merchant details, and user reviews to predict users' ratings for restaurants they have not visited.
Implemented all the 3 major types of Recommendation Systems, namely, Item Based CF Recommendation System, Model-Based Recommendation System and Hybrid Recommendation System.
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