The complexity of music recommendation systems has increased rapidly
in recent years, drawing upon different sources of information: content
analysis, web-mining, social tagging, etc. Unfortunately, the tools to
scientifically evaluate such integrated systems are not readily available;
nor are the base algorithms available. This article describes Graph-RAT
(Graph-based Relational Analysis Toolkit), an open source toolkit that
provides a framework for developing and evaluating novel hybrid systems.
While this toolkit is designed for music recommendation, it has applications
outside its discipline as well. An experiment—indicative of the
sort of procedure that can be configured using the toolkit—is provided to
illustrate its usefulness.
