Context-aware recommender systems (CARS) can be built to adapt the list of item recommendations to different context situations (e.g., time, location, companion, budget, weather, etc.). Users can receive the appropriate recommendations tailored by their preferences in specific contexts.
The DeepCARSKit library is an open-source and deep learning based context-aware recommendation library, where it can be used, modified and distributed under the terms of the MIT License. CARSKit was built upon Recbole v1.0.0 with an environment of Python v3.7+, PyTorch v1.7+. It is specifically designed for context-aware recommendations. It implements context-aware recommendation models based on neural networks. The major components in DeepCARSKit can be viewed as follows.
List of useful resources:
If you used DeepCARSKit, and found it useful, please cite the following work.
@article{deepcarskit, title={DeepCARSKit: A Deep Learning Based Context-Aware Recommendation Library}, author={Zheng, Yong}, journal={Software Impacts}, volume={13}, pages={100292}, year={2022}, publisher={Elsevier} }
Codes available on the Github platform.
Support both hold-out and N-fold cross validations.
The top-N recommendations can be produced and evaluated for a user in a specific context situation.
The libraries support multiple evaluations, e.g., MAE, RMSE, Precision, Recall, F1, NDCG, MAP, MRR, etc.
They were specifically designed for context-aware recommendations.
Several univiersities, research labs and companies utilized CARSKit and DeepCARSKit for research!
"CARSKit has been selected among other recommendation frameworks in the UbiCARS framework, due to the many efficient recommendation algorithms it offers, its easeof use, and the flexibility with which it canbe fine-tuned to work with multi-dimensional data sets." [ref]
"CARSKit is freeware and developed through the Java programming language. It is also convenient to use, thanks to the presence of a user guide associated..." [ref]
CARSKit is listed as one of the Recommender-System Software Libraries & APIs by "RS_c" which is the central platform for the RecSys community. [ref]
Find us on Github.