Mixtape Application: Last.fm Data Characterization

TL;DR

This paper analyzes Last.fm data to understand user profiles, song and artist popularity, and song similarity, aiming to support real-time music recommendation systems.

Contribution

It provides a comprehensive characterization of user and music data from Last.fm, including demographic, popularity, and co-occurrence analyses, useful for recommendation algorithms.

Findings

Identified key user demographic patterns

Characterized song and artist popularity trends

Analyzed song co-occurrence for similarity measures

Abstract

This report analyses data collected from Last.fm and used to create a real-time recommendation system. We collected over 2M songs and 1M tags and 372K user's listening habits. We characterize users' profiles: age, playcount, friends, gender and country. We characterized song, artist and tag popularity, genres of songs. Additionally we evaluated the co-occurrence of songs in users' histories, which can be used to compute similarity between songs.