A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models
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Updated
Jun 7, 2024 - Python
A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models
A Comparative Framework for Multimodal Recommender Systems
A framework for large scale recommendation algorithms.
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
RecTools - library to build Recommendation Systems easier and faster than ever before
購入履歴により自動的に提案する仕組み
This project is an web-based smart education system that uses one of three recommendation algorithms to suggest educational content to users. It's built for the Department of Computer Science, University of Benin, Nigeria.
Modélisation d'un base de données Cinenet , sur le modele de TMBD
How Pytorch implementation of SimpleX model training logic might look like in production
The project of Cogito with Infor
In this project, we have explored the research paper titled Variational Autoencoders for Collaborative Filtering and implemented its findings. We aim to further improve upon the proposed methodology to contribute to the advancement of personalized recommendations and push the boundaries of existing techniques.
A system that will help in a personalized recommendation of courses for an upcoming semester based on the performance of previous semesters.
The complete code and notebooks used for the ACM Recommender Systems Challenge 2019
推荐系统入门教程,在线阅读地址:https://datawhalechina.github.io/fun-rec/
Web App to measure novelty and diversity of Recommender Systems algorithms
in this section will be item based recommender on movies and ratings dataset
Source code for Twitter's Recommendation Algorithm.
This repository contains code for the Recommendation system to find restaurants. An End to End Project developed using Flask and python. The website is hosted on Heroku.
2021년 경상북도 데이터 경진대회 | 추천 알고리즘을 이용한 맞춤형 식품 추천 서비스
Developing recommendation systems to replicate NETFLIX's user experience. Implemented Popularity Ranking, Memory-Based Collaborative Filtering (User-Based and Item-Based), and a Random Recommender.
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