363 Open source projects that are alternatives of or similar to SAE-NAD

The implementation of "Gated Attentive-Autoencoder for Content-Aware Recommendation"

rectorch is a pytorch-based framework for state-of-the-art top-N recommendation

Large scale training of factorization models for Collaborative Filtering with PyTorch

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.

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation - ECIR 2020

AE and VAE Playground in PyTorch

Implementations of autoencoder, generative adversarial networks, variational autoencoder and adversarial variational autoencoder

A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis, PyTorch implementation.

BPR, Bayesian Personalized Ranking (BPR), extremely convenient BPR & Multiple Pairwise Ranking

SoulSeek client with web interface and recommender system

This is a repository of public data sources for Recommender Systems (RS).

This is the official implementation for IVA '19 paper "Analyzing Input and Output Representations for Speech-Driven Gesture Generation".

Caver: a toolkit for multilabel text classification.

Hybrid Recommender System

content discovery... IN 3D

MiraPy: A Python package for Deep Learning in Astronomy

Friends Recommendation and Link Prediction in Social Netowork

Efficient training of deep recommenders on cloud.

A simple Music Recommendation System

Machine Learning (EE 5184) in NTU

Neural Citation Network for Context-Aware Citation Recommendation (SIGIR 2017)

推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/

📖Notes and remarks on Machine Learning related papers

Shared Latent Space VAE's

Multiplicative LSTM for Recommendations

Multi-order Attentive Ranking Model for Sequential Recommendation

kNN-based next-basket recommendation

code for Gogleva et al manuscript

PyTorch implementation of 'An Unsupervised Neural Attention Model for Aspect Extraction' by He et al. ACL2017'

Learning about Haskell with Variational Autoencoders

This is our official implementation for the paper: Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, and Tat-Seng Chua, Adversarial Training Towards Robust Multimedia Recommender System.

Towards open benchmarking for recommender systems https://openbenchmark.github.io/BARS

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

attention mechanism in keras, like Dense and RNN...

Video lstm auto encoder built with pytorch. https://arxiv.org/pdf/1502.04681.pdf

In this code we implement and compared Collaborative Filtering algorithm, prediction algorithms such as neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others.

📈 PyTorch dual-attention LSTM-autoencoder for multivariate Time Series 📈

eForest: Reversible mapping between high-dimensional data and path rule identifiers using trees embedding

Бэйслайн к задаче RetailHero.ai/#2 от @geffy 💪

Sequential recommendation algorithm

Rank and serve recommendations

MoHR: Recommendation Through Mixtures of Heterogeneous Item Relationships

Implementation of Bayesian Personalized Ranking (BPR) for Multiple Feedback Channels

A framework for large scale recommendation algorithms.

A local mapping project using data from OpenStreetMap. Includes overlays, walking directions and historical information.

Bayesian Personalized Ranking using PyTorch

Statistical Learning & Data Mining IV - H2O Presenation & Tutorial

The complete code and notebooks used for the ACM Recommender Systems Challenge 2019

No description or website provided.

A python library for music recommendation

a recommender engine node-js package for general use and easy to integrate.

Global NIPS Paper Implementation Challenge of "Hiding Images in Plain Sight: Deep Steganography"

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀

Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

Colorizing B&W Photos with Neural Networks

Variational autoencoder, denoising autoencoder and other variations of autoencoders implementation in keras

Implemented User Based and Item based Recommendation System along with state of the art Deep Learning Techniques

This is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis

Repository for Attention Algorithm

Experiments with Adversarial Autoencoders using Keras