WrapRec is an easy-to-use open-source toolkit, written in C#, that allows users to easily build a recommender model and evaluate it.WrapRec is not about low-level RecSys algorithms, its instead a framework which Wrap several RecSys toolkits and make it possible to evaluate several algorithms under same evaluation framewrok.
Easy to UseTo perform experiments with WrapWrec all you need to do is to write your settings in a configuration file.
Wrap Multiple Algorithms, Evaluate on Single FrameworkWrapRec is designed to wrap multiple algorithms (from different toolkits) and evaluate your model under a single evaluation framework.
Multiple experiments in one RunIn WrapRec parameters (that you define in the config file) can have multiple values. WrapRec detects those values and run evaluation experiments as many times as the cartesian product of all those parameters.
Context-aware RecommendationWrapRec contain components that makes it easy to perform context-aware recommendations.
Multiple Evaluation MethodsWith WrapRec you can run multiple evaluation methods on a single algorithm and compare the results.
Easily ExtendibleWithout requiring to modify the WrapRec source code, you can define your own extension and plug it into the framework.
How to Use
WrapRec can be simply used by running the executable application. All the settings and parameters should be defined in an xml-based configuration file. Using WrapRec is as easy as running the following command:
Linux and MacIn Linux and Mac you should have .Net Mono installed and the WrapRec can be runned via:
mono wraprec.exe [config.xml]
Configuration FileIn WrapRec all the desired settings and parameters are defined in a signle configuration file. With the configuration file you shoud specify four main things:
ModelsTo specify the models that you use for training the recommender system
DataTo specify where the data is stored and how it should be loaded
SplitsTo define how the data should be splited (for training and evaluation)
EvaluationTo specify the methods and metrics for evalution