Recommender Systems for piano practice (multiple topics)

 Recommender systems are being ubiquitously used to optimally  couple users and products. The goal of this project is to employ standard methods used to produce recommendations [1,4]

in order to provide learners with suitable practice units while they are learning a motor skill, such as playing piano [2]. The resulting project should explore applications of available software [3].

Multiple work directions include the following building blocks:

1:  Automatic creation of  database with practice exercises from internet (e.g. based on pieces available in midi format); automatic labeling according to the complexity level

2:Web-based interface to gather learner-data

3: Testing different approaches to sequence-aware recommendation systems to accelerate learning (e.g. developed for amazon and netflix recommendation  [5])

[1] https://developers.google.com/machine-learning/recommendation/overview/t...

[2] https://arxiv.org/abs/2106.12937

[3] https://github.com/microsoft/recommenders

[4] https://arxiv.org/pdf/1905.06874.pdf

[5] https://aws.amazon.com/blogs/media/whats-new-in-recommender-systems/

*Picture taken from https://realpython.com/build-recommendation-engine-collaborative-filtering/