@inproceedings{d2cb4f81540e4ec397d5339280ab84f4,
title = "Using song social tags and topic models to describe and compare playlists",
abstract = "Playlists are a natural delivery method for music recommendation and discovery systems. Recommender systems offering playlists must strive to make them relevant and enjoyable. In this paper we survey many current means of generating and evaluating playlists. We present a means of comparing playlists in a reduced dimensional space through the use of aggregated tag clouds and topic models. To evaluate the fitness of this measure, we perform prototypical retrieval tasks on playlists taken from radio station logs gathered from Radio Paradise and Yes.com, using tags from Last.fm with the result showing better than random performance when using the query playlist's station as ground truth, while failing to do so when using time of day as ground truth. We then discuss possible applications for this measurement technique as well as ways it might be improved.",
keywords = "Information retrieval, LDA, Metric space, Music, Playlists, Similarity, Social tags, Topic Models",
author = "Ben Fields and Christophe Rhodes and Mark D'Inverno",
note = "This work is supported in part by the Engineering and Physical Sciences Research Council via the Online Music Recognition And Searching II (OMRAS2) project, reference number EP/E02274X/1. Additional support provided as part of the Networked Environments for Music Analysis (NEMA) project, funded by The Andrew W. Mellon Foundation. Thanks also to Paul Lamere for some dataset acquisition assistance. ; 1st Workshop on Music Recommendation and Discovery 2010, WOMRAD 2010 ; Conference date: 26-09-2010 Through 26-09-2010",
year = "2010",
month = sep,
day = "26",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS",
pages = "5--12",
booktitle = "Proceedings of the Workshop on Music Recommendation and Discovery 2010, WOMRAD-2010",
}