Neo4j Provides Musimap with a Real-Time Recommendation and Search Engine for the Music Industry

The Challenge

Musimap cofounder Dr. Pierre Lebecque — a sociology and musicology researcher — was convinced that musical culture was the result of the interweaving of different musical references. On this basis, he decided to create a database that acted as a repository for every published piece of music in the world. Their customers span from streaming services to music promoters and restaurants.

For each song entered into the database, Musimap allocates 55 weighted description criteria that allow their database to perform in-depth searches and title recommendations. To do this, the team first built a solution in a standard SQL database, which experienced performance issues as the size of their database grew.

It also became clear that a visual depiction of their data would allow them to better serve their customers. This naturally led Musimap to explore graph technology.

The Solution

In 2013, having quickly ruled out MySQL or PHP/Flash databases for reasons of performance, Musimap discovered graph databases, and in particular Neo4j, as being the only options that were capable of responding to the company’s requirements.

Shortly after switching to Neo4j, the Musimap team quickly realized the benefits of the solution: easy to use and configure, as well as reduced processing time and query response time. Via a newly-developed API, the company was able to quickly import and personalize its database.

Frédéric Notet, co-founder and chief technical officer of Musimap, is happy with the choice of Neo4j. “When it came to importing our database, Neo4j perfectly met our requirements,” Notet remarks. “We created a script on SQL which called up the Neo4j API. And then Neo4j did the rest, by integrating the whole of our database.”

With its technology now ready for market, Musimap opted for a business model that would allow both for the creation of a recommendation algorithm — used by companies to create playlists and categorize their catalogues — and assistance with creating film music, a service aimed at AV professionals that provides guidance based on movie genre.

Using Neo4j, Musimap was able to define an advanced search mode based on multiple criteria, such as rhythm, instrumentation, 400 complex moods and 100 listening contexts. This provided the tool with the ability to provide recommendations based on nuanced emotions, and make requests for something along the lines of a title that resembles Michael Jackson’s Thriller but in a happier tone.

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