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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.
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# GRU4Rec This is the original Theano implementation of the algorithm of the paper ["Session-based Recommendations With Recurrent Neural Networks"](https://arxiv.org/abs/1511.06939 "Session-based Recommendations With Recurrent Neural Networks"), with the extensions introduced in the paper ["Recurrent Neural Networks with Top-k Gains for Session-based Recommendations"](https://arxiv.org/abs/1706.03847 "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations"). Make sure to always use the latest version as baseline and cite both papers when you do so! The code was optimized for fast execution on the GPU (up to 1500 mini-batch per second on a GTX 1080Ti). According to the Theano profiler, training spends 97.5% of the time on the GPU (0.5% on CPU and 2% moving data between the two). Running on the CPU is not supported, but it is possible with some modificatons to the code. If you are afraid of using Theano, the following official reimplementations are also available. - [Official **PyTorch** version of GRU4Rec](https://github.com/hidasib/GRU4Rec_PyTorch_Official) - [Official **Tensorflow** version of GRU4Rec](https://github.com/hidasib/GRU4Rec_Tensorflow_Official) *NOTE:* These have been validated against the original, but due to how more modern deep learning frameworks operate, they are 1.5-4x slower than this version. Other reimplementations might be available in the future, depending on the research community's interest level. **IMPORTANT!** Avoid using unofficial reimplementations. We thorougly examined 6 third party reimplementations (PyTorch/Tensorflow, standalone/framework) in ["The Effect of Third Party Implementations on Reproducibility"](https://arxiv.org/abs/2307.14956) and all of them were flawed and/or missed important features, that resulted in up to **99% lower recommendation accuracy** and up to **335 times longer training times**. Other reimplementations we have found since then are no better. You can train and evaluate the model on your own session data easily using `run.py`. Usage information below. Scroll down for infromation on reproducing results on public datasets and hyperparameter tuning! **LICENSE:** See [license.txt](license.txt) for details. Main guidelines: for research and education purposes the code is and always will be free to use. Using the code or parts of it in commercial systems requires a licence. If you've been using the code or any of its derivates in a commercial system, contact me! **CONTENTS:** [Requirements](#requirements "Requirements") [Theano configuration](#theano-configuration "Theano configuration") [Usage](#usage "Usage") [Execute experiments using `run.py`](#execute-experiments-using-runpy "Execute experiments using run.py") [Examples](#examples "Examples") [Using GRU4Rec in code or the interpreter](#using-gru4rec-in-code-or-the-interpreter "Using GRU4Rec in code or the interpreter") [Notes on sequence-aware and session-based models](#notes-on-sequence-aware-and-session-based-models "Notes on sequence-aware and session-based models") [Notes on parameter settings](#notes-on-parameter-settings "Notes on parameter settings") [Speed of training](#speed-of-training "Speed of training") [Reproducing results on public datasets](#reproducing-results-on-public-datasets "Reproducing results on public datasets") [Hyperparameter tuning](#hyperparameter-tuning "Hyperparameter tuning") [Executing on CPU](#executing-on-cpu "Executing on CPU") [Major updates](#major-updates "Major updates") ## Requirements - **python** --> Use python `3.6.3` or newer. The code was mostly tested on `3.6.3`, `3.7.6` and `3.8.12`, but was briefly tested on other versions. Python 2 is NOT supported. - **numpy** --> `1.16.4` or newer. - **pandas** --> `0.24.2` or newer. - **CUDA** --> Needed for the GPU support of Theano. The latest CUDA version Theano was tested with (to the best of my knowledg
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matched fp:f903f35f07c7b7b0, llm:Repository description: 'GRU4Rec is the original Theano implementation of the algorithm in "Session-based Recommendations with Recurrent Neural Networks"... optimized for execution on the GPU.'
matched fp:f903f35f07c7b7b0, llm:Repository description: 'GRU4Rec is the original Theano implementation of the algorithm in "Session-based Recommendations with Recurrent Neural Networks"... optimized for execution on the GPU.'