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Recommendation Solutions

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.

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Why Choose Us


Open-Source Libraries

Codes available on the Github platform.


Training Modes

Support both hold-out and N-fold cross validations.


Context-Aware Evaluations

The top-N recommendations can be produced and evaluated for a user in a specific context situation.


Multiple Metrics

The libraries support multiple evaluations, e.g., MAE, RMSE, Precision, Recall, F1, NDCG, MAP, MRR, etc.


Unique for CARS

They were specifically designed for context-aware recommendations.


Popular Libraries

Several univiersities, research labs and companies utilized CARSKit and DeepCARSKit for research!

Reviews from our users

What People Say

University of Cyprus,
Nicosia, Cyprus

Research Lab

"CARSKit has been selected among other recommendation frameworks in the UbiCARS framework, due to the many efficient recommendation algorithms it offers, its ease
of use, and the flexibility with which it can
be fine-tuned to work with multi-dimensional data sets.
" [ref]

University of Salerno,
Fisciano, Italy

Research Lab

"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]

RecSys Community

The "RS_c" platform

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]

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Current and Past Sponsors

2022 - 2023

Google Cloud Platform

We thank the support and grant by Google Cloud Platform which provides us the cloud computing resources (e.g., GPU) to test and evaluate this library.

2022 - 2023

Amazon Web Services

We thank the support and grant by Amazon Web Services which provides us the cloud computing resources (e.g., GPU) to test and evaluate this library.

Find us on Github.

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