Massimo Quadrana (mquad)

mquad

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Company:Politecnico di Milano

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Massimo Quadrana's repositories

sars_tutorial

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

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hgru4rec

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

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recsys-course-2016-pub

Course on Recommender Systems 2016 - Politecnico di Milano

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DNN_Lab_UPF

Code for the Deep Neural Network seminar at UPF

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deep-learning-keras-tensorflow

# Deep Learning with Keras and Tensorflow

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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.

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lightfm

A Python implementation of LightFM, a hybrid recommendation algorithm.

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pandas-tutorial

Material for the pandas hands-on at UPF

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Crepe

Character-level Convolutional Networks for Text Classification

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hed-dlg-truncated

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

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homework

Imitation Learning Homework 1

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hred-qs

Hierarchical Recurrent Encoder Decoder for Query Suggestion

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implicit

Fast Python Collaborative Filtering for Implicit Datasets

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keras-contrib

Keras community contributions

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libfm

Library for factorization machines

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mquad.github.io

My personal webpage

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recordlinkage

A toolkit for record linkage and deduplication written in Python

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reinforcement-learning

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

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rl_book_code

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

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spotlight

Deep recommender models using PyTorch.

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unbiased-implicit-rec

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

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word-rnn

Recurrent Neural Network that predicts word-by-word

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www-15-tutorial

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

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