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2015 IEEE International Conference on Data Mining Workshop (ICDMW) (2015)
Atlantic City, NJ, USA
Nov. 14, 2015 to Nov. 17, 2015
ISSN: 2375-9259
ISBN: 978-1-4673-8492-6
pp: 1668-1671
Recommender system has been demonstrated as one of the most useful tools to assist users' decision makings. Several recommendation algorithms have been developed and implemented by both commercial and open-source recommendation libraries. Context-aware recommender system (CARS) emerged as a novel research direction during the past decade and many contextual recommendation algorithms have been proposed. Unfortunately, no recommendation engines start to embed those algorithms in their kits, due to the special characteristics of the data format and processing methods in the domain of CARS. This paper introduces an open-source Java-based context-aware recommendation engine named as CARSKit which is recognized as the 1st open source recommendation library specifically designed for CARS. It implements the state-of-the-art context-aware recommendation algorithms, and we will showcase the ease with which CARSKit allows recommenders to be configured and evaluated in this demo.
Context, Algorithm design and analysis, Automobiles, Engines, Libraries, Prediction algorithms, Standards
Yong Zheng, Bamshad Mobasher, Robin Burke, "CARSKit: A Java-Based Context-Aware Recommendation Engine", 2015 IEEE International Conference on Data Mining Workshop (ICDMW), vol. 00, no. , pp. 1668-1671, 2015, doi:10.1109/ICDMW.2015.222
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