The guitar is one of the most popular musical instruments, and numerous pedagogical tools have been developed to support learners. They rely on vast collections of songs, sheet music, and tablatures, making it challenging for guitarists to navigate and identify pieces that are both pedagogically relevant and aligned with their musical interests.

We introduce a simple multi-criteria rule-based model to assess both the difficulty of learning a piece and the skill level of a guitarist, taking into account musical and technical criteria. The models provides personalized recommendations that help learners progress efficiently, considering parts within songs, but also multiple versions of the same part, accounting for simplified adaptations or different playing styles, and finally exercises used to progressively learn each part version.

We implement and evaluate this approach in the context of rhythm guitar in popular music, using a dataset designed for the proprietary application Guitar Social Club. Expert evaluation of 77 recommendations for 8 user profiles of varying levels indicate that in 82% of cases, the model provides relevant recommendations. While the full dataset remains proprietary, we release under open licenses the code along with a sub-corpus containing annotated difficulties for 319 versions of 110 parts from 40 songs.