DataGenCARS: A generator of synthetic data for the evaluation of context-aware recommendation systems

Abstract Context-Aware Recommender Systems (CARS) have started to attract significant research attention in the last years, due to the interest of considering the context of the user in order to offer him/her more appropriate recommendations. However, the evaluation of CARS is a challenge, due to the scarce availability of appropriate datasets that incorporate context information related to the ratings provided by the users. In this paper, we present DataGenCARS, a complete Java-based synthetic dataset generator that can be used to obtain the required datasets for any type of scenario desired, allowing a high flexibility in the obtention of appropriate data that can be used to evaluate CARS. The generator presents features such as: a flexible definition of user schemas, user profiles, types of items, and types of contexts; a realistic generation of ratings and attributes of items; the possibility to mix real and synthetic datasets; functionalities to analyze existing datasets as a basis for synthetic data generation; and support for the automatic mapping between item schemas and Java classes. Moreover, an experimental evaluation illustrates the interest and the benefits provided by DataGenCARS.

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