Large Scale Discovery of Seasonal Music From User Data

TL;DR

This paper presents a method for classifying seasonal music, specifically Christmas songs, using large-scale user listening data and Gaussian Mixture Models, highlighting the potential for improved media recommendation systems.

Contribution

It introduces a scalable approach to identify seasonal music from user data, leveraging Gaussian Mixture Models for effective classification.

Findings

High accuracy in classifying Christmas music

Effective use of large-scale user listening data

Demonstrates potential for enhancing recommendation systems

Abstract

The consumption history of online media content such as music and video offers a rich source of data from which to mine information. Trends in this data are of particular interest because they reflect user preferences as well as associated cultural contexts that can be exploited in systems such as recommendation or search. This paper classifies songs as seasonal using a large, real-world dataset of user listening data. Results show strong performance of classification of Christmas music with Gaussian Mixture Models.