Factorization Machine models in PyTorch
-
Updated
Apr 8, 2024 - Python
Factorization Machine models in PyTorch
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
A Deep Learning Based Context-Aware Recommendation Library
implementation of federated neural collaborative filtering algorithm
PyTorch Implemenation for Neural Graph Collaborative Filtering
Neural recommendation models in Python, using Tensorflow 2.0 & Keras.
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.
Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)
Neural recommender system implementation in TensorFlow.
Collaborative Filtering With User or Item Feature
Neural Collaborative Filtering with MovieLens in pytorch
Seoul Tourism Recommendation System
Neural Collaborative Filtering with MovieLens dataset(WWW, 2017)
A pytorch implementation of He et al. "Neural Collaborative Filtering" at WWW'17
Neural Collaborative Filtering Revised
Implementation of paper Neural Collaborative Filtering in PyTorch.
PyTorch Implementation for Neural Graph Collaborative Filtering
PyTorch Implementation for J-NCF (ACM Transactions on Information Systems, 2019)
2020 Capstone Design
pytorch version of NCF
Add a description, image, and links to the neural-collaborative-filtering topic page so that developers can more easily learn about it.
To associate your repository with the neural-collaborative-filtering topic, visit your repo's landing page and select "manage topics."