Abstract
Music recommendation systems built on top of music information retrieval (MIR) technologies are usually designed to provide new ways to discover and listen to digital music collections. However, they do not typically assist in another important aspect of musical activity, music learning. In this study we present the application Hotttabs, an online music recommendation system dedicated to guitar learning. Hotttabs makes use of The Echo Nest music platform to retrieve the latest popular or "hot" songs based on editorial, social and charts/sales criteria, and YouTube to find relevant guitar video tutorials. The audio tracks of the YouTube videos are processed with an automatic chord extraction algorithm in order to provide a visual feedback of the chord labels synchronised with the video. Guitar tablatures, a form of music notation showing instrument fingerings, are mined from the web and their chord sequences are extracted. The tablatures are then clustered based on the songs' chord sequences complexity so that guitarists can pick up those adapted to their performance skills.