Abstract:
Frameworks that aid the development of Recommender Systems (RSs) are extremely important, since they reduce their development cost by offering reusable tools, as well as implementations of common strategies and popular models. However, it is still hard to find a framework that also provides full abstraction over data set conversion, support for deep learning-based approaches, extensible models and reproducible evaluations. This work introduces a new framework that not only provides several modules to avoid repetitive development work, but also to assist practitioners with these existing challenges. Our evaluation procedure ensures that RSs developed using this new approach are consistent and extensible, by analysing their predictive performance and certain characteristics of their implementation.
