#Collaborative-filtering
Showing 28 of 28 repositories tagged #collaborative-filtering, ranked by stars
AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
RSTutorials: A Curated List of Must-read Papers on Recommender System.
A unified, comprehensive and efficient recommendation library
Fast Python Collaborative Filtering for Implicit Feedback Datasets
This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
Neural Collaborative Filtering
Deep learning for recommender systems
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
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.
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
pytorch version of neural collaborative filtering
Versatile End-to-End Recommender System
recommender system tutorial with Python
Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"
This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).
基于协同过滤和spark-als的电影推荐系统
Tutorials about AutoML
UC Berkeley team's submission for RecSys Challenge 2018
Movie Recommender System with Django.
This repository contains the code for building movie recommendation engine.
The hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset.
building a recommendation system using graph search methodologies. We will be comparing these different approaches and closely observe the limitations of each.
implement the machine learning algorithms by python for studying
Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.
A repository for a machine learning project about developing a hybrid movie recommender system.
This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.
A Movie Recommender System using Restricted Boltzmann Machine (RBM), approach used is collaborative filtering.