https://github.com/florianwilhelm/mlstm4reco
Multiplicative LSTM for Recommendations
https://github.com/florianwilhelm/mlstm4reco
pytorch recommender-system spotlight
Last synced: 6 months ago
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Multiplicative LSTM for Recommendations
- Host: GitHub
- URL: https://github.com/florianwilhelm/mlstm4reco
- Owner: FlorianWilhelm
- License: mit
- Created: 2018-04-12T11:45:56.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-08-07T17:11:23.000Z (about 7 years ago)
- Last Synced: 2025-04-14T09:14:37.701Z (6 months ago)
- Topics: pytorch, recommender-system, spotlight
- Language: Python
- Homepage: https://florianwilhelm.info/2018/08/multiplicative_LSTM_for_sequence_based_recos/
- Size: 21.5 KB
- Stars: 20
- Watchers: 3
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.rst
- License: LICENSE.txt
- Authors: AUTHORS.rst
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README
==========
mlstm4reco
==========
Benchmark multiplicative LSTM vs. ordinary LSTM. Read this `blog post`_ about the evaluation.
Description
===========
Create a conda environment with::
conda env create -f environment-abstract.yml
or use::
conda env create -f environment-concrete.yml
to perfectly replicate the environment.
Then activate the environment with::
source activate mlstm4reco
and install it with::
python setup.py develop
Then change into the ``experiments`` directory and run:
./run.py 10m -m mlstm
to run the ``mlstm`` model on the Movielens 10m dataset. Check out
``./run.py -h`` for more help.
Note
====
This project has been set up using PyScaffold 3.0.2. For details and usage
information on PyScaffold see http://pyscaffold.org/.
.. _`blog post`: https://florianwilhelm.info/2018/08/multiplicative_LSTM_for_sequence_based_recos/