Cornac
Cornac: a comparative framework for multimodal recommender systems. 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 url{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.
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References in zbMATH (referenced in 3 articles , 1 standard article )
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Sorted by year (- Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot, Shuo Zhou, Mustafa Chasmai, Lawrence Schobs: PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python (2021) arXiv
- Vito Walter Anelli, Alejandro BellogĂn, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia: Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation (2021) arXiv
- Salah, Aghiles; Truong, Quoc-Tuan; Lauw, Hady W.: Cornac: a comparative framework for multimodal recommender systems (2020)