Key Features
Multimodality
Data infrastructures make it convenient to work with auxiliary information and enable seamless cross-modality comparisons.
Scalability
A rich collection of iterators for easy stochastic optimization. Model implementations make use of Cython to achieve C/C++ performance.
Reproducibility
Full control over random number generators, open-access to existing algorithms and built-in datasets for reproducible research.
Citation
If you use Cornac in a scientific publication, we would appreciate citations to the following paper:
Cornac: A Comparative Framework for Multimodal Recommender Systems, Salah et al., JMLR 21, pp. 1-5, 2020.
Bibtex entry:
@article{cornac, author = {Aghiles Salah and Quoc-Tuan Truong and Hady W. Lauw}, title = {Cornac: A Comparative Framework for Multimodal Recommender Systems}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {95}, pages = {1-5}, url = {http://jmlr.org/papers/v21/19-805.html} }