Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2020
Abstract
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.
Keywords
Comparison, Multimodality, Recommendation algorithms, Software
Discipline
Databases and Information Systems | Data Science
Research Areas
Data Science and Engineering
Publication
Journal of Machine Learning Research
Volume
21
Issue
95
First Page
1
Last Page
5
ISSN
1532-4435
Publisher
JMLR
Embargo Period
5-20-2021
Citation
SALAH, Aghiles; TRUONG, Quoc Tuan; and LAUW, Hady W..
Cornac: A comparative framework for multimodal recommender systems. (2020). Journal of Machine Learning Research. 21, (95), 1-5.
Available at: https://ink.library.smu.edu.sg/sis_research/5949
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://www.jmlr.org/papers/v21/19-805.html