Modeling Artist Influence Paths for Music Selection and Recommendation: A Purely Network-based Approach

Music City Center 

In light of the central role that online recommendations have taken in people's lives, and given the limitations and wide room for improvement for music recommendation systems currently in place, this work proposes a novel content-based approach for generating music recommendations solely based on the detection of semantic and sonic connections between musical artists and the construction and programmatic analysis of a latent directed graph embedded in such connections. The core idea behind this approach is that traversing such a graph essentially emulates the humanly unfeasible but highly intuitive process of reading through an arbitrary-length sequence of high-quality music reviews, using any given artist as a starting point and hoping from one review to the next according to the list of different artists found in each read, and hierarchically prioritizing the queue by semantic and sonic similarity, two metrics that, in tandem, can be plausibly interpreted as musical influence in this context. First, I describe the background and motivation for developing this framework, and explain its novelty and main benefits. I then explain how connections between artists are drawn, how the graph is built, and how this the graph can be enhanced with sonic attributes and used programmatically to produce music recommendations in the form of optimal sequences of artists and/or songs. Finally, I presents an application of this framework using reviews from several reputable online music publications, presents some of the resulting graph properties, which prove to be highly desirable in terms of connectivity, and presents a publicly available interactive tool built on top of that graph.I conclude by summarizing the main strengths of this framework, discussing its limitations, and pointing to some potential further enhancements.