Context-Aware
Recommendation Solutions

The CARSKit Library

The CARSKit library is an open-source and Java-based context-aware recommendation engine, where it can be used, modified and distributed under the terms of the GNU General Public License. CARSKit was built upon LibRec v1.3 and JDK 7. It is specifically designed for context-aware recommendations. It implements context-aware collaboative filtering (CF) algorithms based on traditional CF, e.g., user-based CF, matrix factorization, sparse linear methods, etc. The framework of CARSKit can be viewed as follows.

There are two categories of recommendation algorithms included in the CARSKit library:

• Traditional recommendation algorithms: The implementations of those algorithms (such as UserKNN, BiasedMF, SVD++, SLIM, etc) are from LibRec V1.3. Those algorithms can be used in two ways: 1). run a traditional recommendation algorithm directly on the context-aware data set to compete with the context-aware recommendation algorithms; 2). run contextual recommendation algorithms based on transformation, e.g., run a traditional recommendation algorithm after data transformation (e.g., by item splitting).
• Context-aware recommendation algorithms: CARSKit simply divides it into two categories: Transformation Algorithms and Adaptation Algorithms. The transformation algorithms try to convert the mulidimensional recommendation problem into traditional 2-dimensional problem, so that the traditional recommendation algorithms can still be used. In contrast to those algorithms based on transformation algorithms, the adaptation algorithms focus on their effect on building algorithms by adapting to the multidimensional rating space. Most of the algorithms inside belong to the contextual modeling algorithms, such as Context-aware Matrix Facatorization (CAMF), Tensor Factorization (TF), Contextual Sparse Linear Method (CSLIM), Factorization Machines (FM), etc.

List of useful resources:

• CARSKit: Github, API Doc
• Yong Zheng, Bamshad Mobasher, Robin Burke. "CARSKit: A Java-Based Context-aware Recommendation Engine", Proceedings of the 15th IEEE International Conference on Data Mining (ICDM) Workshops, pp. 1668-1671, Atlantic City, NJ, USA, Nov 2015
• Yong Zheng. "A User's Guide to CARSKit", arXiv preprint arXiv:1511.03780, 2015.

If you used CARSKit, and found it useful, please cite the following work.

@inproceedings{zheng2015carskit,
  title={CARSKit: A Java-based context-aware recommendation engine},
  author={Zheng, Yong and Mobasher, Bamshad and Burke, Robin},
  booktitle={2015 IEEE International Conference on Data Mining Workshop},
  pages={1668--1671},
  year={2015},
  organization={IEEE}
}