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Adversarial Autoencoders for Recommendation Tasks
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README.md

aae-recommender

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Adversarial Autoencoders for Recommendation Tasks

Dependencies

  • torch
  • numpy
  • scipy
  • sklearn
  • gensim
  • pandas
  • joblib

If possible, numpy and scipy should be installed as system packages. The dependencies gensim and sklearn can be installed via pip. For pytorch, please refer to their installation instructions that depend on the python/CUDA setup you are working in.

Installation

You can install this package and all necessary dependencies via pip.

pip install -e .

Running

The main.py file is an executable to run an evaluation of the specified models. The dataset and year are mandatory arguments. The dataset is expected to be a path to a tsv-file, of which the format is described next.

Dataset Format

The expected dataset Format is a tab-separated with columns:

  • owner id of the document
  • set comma separated list of items
  • year year of the document
  • title of the document

The columns 'owner' and 'set' are expected to be the first two ones, since they are mandatory. An arbitrary number of supplementary information columns can follow. The current implementation, however, makes use of the year property for splitting the data into train and test sets. Also, title-enhanced recommendation models rely on the title property to be present.

Concrete datasets

So far, we worked with the PubMed citations dataset from CITREC. We converted the provided SQL dumps into the dataset format above. We plan to also publish our converted tsv version of the CITREC PubMed dataset. The references in the CITREC TREC Genomics dataset are not disambiguated. Therefore we operate only the PubMed dataset for citation recommendation.

For subject label recommendation, we used the the economics dataset EconBiz, provided by ZBW. We are currently asserting that copyright issues do not prevent us from publishing the metadata of the documents in the aforementioned format.

References and cite

Please see our papers for additional information on the models implemented and the experiments conducted:

If you use our code in your own work please cite one of these papers:

@inproceedings{Vagliano:2018,
     author = {Vagliano, Iacopo and Galke, Lukas and Mai, Florian and Scherp, Ansgar},
     title = {Using Adversarial Autoencoders for Multi-Modal Automatic Playlist Continuation},
     booktitle = {Proceedings of the ACM Recommender Systems Challenge 2018},
     series = {RecSys Challenge '18},
     year = {2018},
     isbn = {978-1-4503-6586-4},
     location = {Vancouver, BC, Canada},
     pages = {5:1--5:6},
     articleno = {5},
     numpages = {6},
     url = {http://doi.acm.org/10.1145/3267471.3267476},
     doi = {10.1145/3267471.3267476},
     acmid = {3267476},
     publisher = {ACM},
     address = {New York, NY, USA},
     keywords = {adversarial autoencoders, automatic playlist continuation, multi-modal recommender, music recommender systems, neural networks},
}

@inproceedings{Galke:2018,
     author = {Galke, Lukas and Mai, Florian and Vagliano, Iacopo and Scherp, Ansgar},
     title = {Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels},
     booktitle = {Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization},
     series = {UMAP '18},
     year = {2018},
     isbn = {978-1-4503-5589-6},
     location = {Singapore, Singapore},
     pages = {197--205},
     numpages = {9},
     url = {http://doi.acm.org/10.1145/3209219.3209236},
     doi = {10.1145/3209219.3209236},
     acmid = {3209236},
     publisher = {ACM},
     address = {New York, NY, USA},
     keywords = {adversarial autoencoders, multi-modal, neural networks, recommender systems, sparsity},
}
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