There are 11 repositories under hybrid-recommender-system topic.
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
A recommender system built for book lovers.
This repository contains the code for building movie recommendation engine.
A repository for a machine learning project about developing a hybrid movie recommender system.
A Hybrid recommendation engine built on deep learning architecture, which has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
Hybrid recommedation for talents
A Hybrid Recommendation system which uses Content embeddings and augments them with collaborative features. Weighted Combination of embeddings enables solving cold start with fast training and serving
A Content Based And A Hybrid Recommender System using content based filtering and Collaborative filtering
This is a book recommendation engine built using a hybrid model of Collaborative filtering, Content Based Filtering and Popularity Matrix.
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"
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
Movie recommendation system based on hybrid recommender and clustering
Recommendation System Algorithm
A hybrid recommender system for suggesting CDN (content delivery network) providers to various websites
This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.
Auto encoders based recommendation system
Comparison of performance evaluation of the baseline and hybrid recommendation systems using various metrics, to prove that hybrid systems perform better
Hybrid Recommendation System for IMDB data set In Python from Scratch (can be scaled to any applications)
Recommends movies using Collaborative and Content based filtering techniques
This repository contains the code for a book recommendation system that uses natural language processing techniques to recommend books to users based on their preferences.
Amar deep architectures for hybrid recommenders with GNNs
BoardGameGeek Recommender System is a start-to-finish project, from sourcing the data to a hybrid recommender system utilizing both content-based and collaborative filtering.
Food Finder: An interface for a multi-user recommendation system.
The objective of the competition was to create the best recommender system for a book recommendation service by providing 10 recommended books to each user. The evaluation metric was MAP@10.
Recommendation engine with a .97 AUC achieved using clustering techniques to create user features. Data represents Olist marketplace transactions and was retrieved from kaggle.com.
Set of music recommendation algorithms we implemented to join the annual RecSys Competition at Politecnico di Milano in 2017.
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
Create a hybrid recommendation system to suggest the most relevant movies for a user
A hybrid group recommendation system for film and TV content using Letterboxd profile data
This is an ecommerce recommendation system that is measured with weighted user rating and content cosine similarity.
Explore the Recommendation System Interview Prep Guide! This GitHub repository provides curated interview questions and answers for Data Scientists. Elevate your knowledge of recommendation systems, navigate technical interviews with confidence, and succeed in the dynamic field of data science focused on recommendation system applications.
Проект создания рекомендательной системы для библиотеки
Building powerful and personalized, recommendation engines with Python