mquad Goto Github PK

bd-tree

crepe

Character-level Convolutional Networks for Text Classification

deep-learning-keras-tensorflow

# Deep Learning with Keras and Tensorflow

dnn_lab_upf

Code for the Deep Neural Network seminar at UPF

gru4rec

GRU4Rec is the cleaned & simplified implementation of the algorithm of the "Session-based Recommendations with Recurrent Neural Networks" paper, published at ICLR 2016. The code is stripped of features that we had found to be unhelpful in increasing accuracy.

hed-dlg-truncated

Hierarchical Encoder Decoder RNN (HRED) with Truncated Backpropagation Through Time (Truncated BPTT)

hgru4rec

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

homework

Imitation Learning Homework 1

hred-qs

Hierarchical Recurrent Encoder Decoder for Query Suggestion

implicit

Fast Python Collaborative Filtering for Implicit Datasets

keras-contrib

Keras community contributions

libfm

Library for factorization machines

lightfm

A Python implementation of LightFM, a hybrid recommendation algorithm.

mquad.github.io

My personal webpage

pandas-tutorial

Material for the pandas hands-on at UPF

playlistrec

recordlinkage

A toolkit for record linkage and deduplication written in Python

recsys-course-2016-pub

Course on Recommender Systems 2016 - Politecnico di Milano

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

rl_book_code

Code exercises from Sutton and Barto's Introduction to Reinforcement Learning

sars_tutorial

Repository for the tutorial on Sequence-Aware Recommender Systems held at TheWebConf 2019 and ACM RecSys 2018

spotlight

Deep recommender models using PyTorch.

unbiased-implicit-rec

(WSDM2020) "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback"

word-rnn

Recurrent Neural Network that predicts word-by-word

www-15-tutorial

Materials for the WWW 2015 tutorial on online experiments for computational social science