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Cornac: A comparative framework for multimodal recommender systems

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journal contribution
posted on 30.03.2021, 12:48 by Aghiles SALAH, TRUONG QUOC TUAN (SMU), Hady Wirawan LAUW

Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://github.com/PreferredAI/cornac, and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests to ensure code quality, but it is also accompanied with a detailed documentation, tutorials, examples, and several built-in benchmarking data sets.

Funding

National Research Foundation Singapore (NRF-NRFF2016-07)

History

Publication Date

01/05/2020

Journal

Journal of Machine Learning Research

Volume

21

Issue

95

Pages

1-5

ISSN

1532-4435

School

School of Computing and Information Systems

IRIS ID

136367937

Exports