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furongpeng's Projects

admm icon admm

Solving Statistical Optimization Problems Using the ADMM Algorithm

awesome-rnn icon awesome-rnn

Recurrent Neural Network - A curated list of resources dedicated to RNN

cdae icon cdae

Collaborative Denoising Auto-Encoder for Top-N Recommender Systems

cf-nade icon cf-nade

A implementation of CF-NADE. Yin Zheng, et. al. "A Neural Autoregressive Approach to Collaborative Filtering", accepted by ICML 2016.

crab icon crab

Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).

electronic-wechat icon electronic-wechat

:speech_balloon: A better WeChat on macOS and Linux. Built with Electron by Zhongyi Tong.

emacs.d icon emacs.d

An Emacs configuration bundle with batteries included

fancl icon fancl

Fast Low-Rank Matrix Learning with Nonconvex Regularization. Matlab Code

fastfm icon fastfm

fastFM: A Library for Factorization Machines

fpmc icon fpmc

Python implementation of "Factorizing Personalized Markov Chains for Next-Basket Recommendation"

gbdt icon gbdt

simple multi-class GBDT

gpmf-gbp-aaai-20 icon gpmf-gbp-aaai-20

Demonstration code for Scalable Probabilistic Matrix Factoriztion with Graph-Based Priors

gru4rec icon gru4rec

GRU4Rec is the original Theano implementation of the algorithm in "Session-based Recommendations with Recurrent Neural Networks" paper, published at ICLR 2016 and its follow-up "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations". The code is optimized for execution on the GPU.

hasc icon hasc

A Hierarchical Attention Model for Social Contextual Image Recommendation, TKDE2019

hdp-faster icon hdp-faster

Hierarchical Dirichlet Process (with Split-Merge Operations), originally by Chong Wang

hgru4rec icon hgru4rec

Code for our ACM RecSys 2017 paper "Personalizing Session-based Recommendation with Hierarchical Recurrent Neural Networks"

how-to-kaggle icon how-to-kaggle

A research into the workflow for Kaggle competition (and data science in general) collaboratively

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