Search Results for author: Balázs Hidasi

Found 7 papers, 4 papers with code

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

2 code implementations13 Jun 2017 Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi

Session-based recommendations are highly relevant in many modern on-line services (e. g. e-commerce, video streaming) and recommendation settings.

Session-Based Recommendations

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

7 code implementations ICLR 2018 Balázs Hidasi, Alexandros Karatzoglou

RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.

Collaborative Filtering Data Augmentation +1

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

General factorization framework for context-aware recommendations

no code implementations18 Jan 2014 Balázs Hidasi, Domonkos Tikk

We demonstrate the framework's potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets.

Context-aware recommendations from implicit data via scalable tensor factorization

no code implementations29 Sep 2013 Balázs Hidasi, Domonkos Tikk

Albeit the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case.

Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback

no code implementations5 Apr 2012 Balázs Hidasi, Domonkos Tikk

In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS.

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