Artist based personalization
By Data Science Team, Cepstra Research LLP
In music personalization, listeners have a tendency to incline towards a single artist or if no similar songs available from the same artist listeners inclined towards listening songs from other artists have a similar style, language or from the artists who might belong to the same ethnic background. For example at mass, listeners may not like to switch the songs from Justin Bieber to Nusrat Fateh Ali Khan or from Skrillex to Alan Jackson. At Cepstra, we developed a pure Artificial Intelligence-based system that identifies and group artists with a similar style based on the information available on the world wide web.
We have a collection of artists’ information, including their career, area of expertise, music albums and songs, mixes, and other music-related productions gathered from various sources throughout the internet.
We executed an algorithm based on Latent semantic analysis, analyzing the relationship between a set of documents and the terms they contain by producing a set of concepts related to the materials.
LSA can be mathematically shown as,
Where,
The collections of crawled content are converted into a matrix with the importance of different concepts throughout the artists’ information catalog, The model decomposed into its constituent parts in order to make precise subsequent matrix calculations simpler using Truncated Singular-Value Decomposition. This matrix is further normalized to its unit norm and based on similarity calculations, we create multiple groups of similar artists and their similarity strength.
